Sensors

2022

Jump to: 2021

11 pages, 6356 KB

Open AccessArticle

An AVMD-DBN-ELM Model for Bearing Fault Diagnosis

by Xue Lei, Ningyun Lu, Chuang Chen and Cunsong Wang

Sensors 2022, 22(23), 9369; https://doi.org/10.3390/s22239369 - 1 Dec 2022

Cited by 12 | Viewed by 2115

Abstract

Rotating machinery often works under complex and variable working conditions; the vibration signals that are widely used for the health monitoring of rotating machinery show extremely complicated dynamic frequency characteristics. It is unlikely that a few certain frequency components are used as the [...] Read more.

Rotating machinery often works under complex and variable working conditions; the vibration signals that are widely used for the health monitoring of rotating machinery show extremely complicated dynamic frequency characteristics. It is unlikely that a few certain frequency components are used as the representative fault signatures for all working conditions. Aiming at a general solution, this paper proposes an intelligent bearing fault diagnosis method that integrates adaptive variational mode decomposition (AVMD), mode sorting based deep belief network (DBN) and extreme learning machine (ELM). It can adaptively decompose non-stationery vibration signals into temporary frequency components and sort out a set of effective frequency components for online fault diagnosis. For online implementation, a similarity matching method is proposed, which can match the online-obtained frequency-domain fault signatures with the historical fault signatures, and the parameters of AVMD-DBN-ELM model are set to be the same as the most similar case. The proposed method can decompose vibration signals into different modes adaptively and retain effective modes, and it can learn from the idea of an attention mechanism and fuse the results according to the weight of MIV. It also can improve the timeliness of the fault diagnosis. For comprehensive verification of the proposed method, the bearing dataset from the University of Ottawa is used, and some recent methods are repeated for comparative analysis. The results can prove that our proposed method has higher reliability, higher accuracy and higher efficiency. Full article

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23 pages, 10387 KB

Open AccessArticle

Insulator-Defect Detection Algorithm Based on Improved YOLOv7

by Jianfeng Zheng, Hang Wu, Han Zhang, Zhaoqi Wang and Weiyue Xu

Sensors 2022, 22(22), 8801; https://doi.org/10.3390/s22228801 - 14 Nov 2022

Cited by 114 | Viewed by 12417

Abstract

Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes [...] Read more.

Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds. Full article

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15 pages, 1519 KB

Open AccessArticle

Detecting Enclosed Board Channel of Data Acquisition System Using Probabilistic Neural Network with Null Matrix

by Dapeng Zhang, Zhiling Lin and Zhiwei Gao

Sensors 2022, 22(15), 5559; https://doi.org/10.3390/s22155559 - 25 Jul 2022

Viewed by 1819

Abstract

The board channel is a connection between a data acquisition system and the sensors of a plant. A flawed channel will bring poor-quality data or faulty data that may cause an incorrect strategy. In this paper, a data-driven approach is proposed to detect [...] Read more.

The board channel is a connection between a data acquisition system and the sensors of a plant. A flawed channel will bring poor-quality data or faulty data that may cause an incorrect strategy. In this paper, a data-driven approach is proposed to detect the status of the enclosed board channel based on an error time series obtained from multiple excitation signals and internal register values. The critical faulty data, contrary to the known healthy data, are constructed by using a null matrix with maximum projection and are labelled as training examples together with healthy data. Finally, the status of the enclosed board channel is validated by a well-trained probabilistic neural network. The experimental results demonstrate the effectiveness of the proposed method. Full article

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29 pages, 3010 KB

Open AccessArticle

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

by Anthony D. Cho, Rodrigo A. Carrasco and Gonzalo A. Ruz

Sensors 2022, 22(14), 5323; https://doi.org/10.3390/s22145323 - 16 Jul 2022

Cited by 3 | Viewed by 3279

Abstract

The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This estimation can be [...] Read more.

The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications. Full article

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17 pages, 4565 KB

Open AccessArticle

Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

by Mohamed-Ali Tnani, Paul Subarnaduti and Klaus Diepold

Sensors 2022, 22(13), 4813; https://doi.org/10.3390/s22134813 - 25 Jun 2022

Cited by 17 | Viewed by 3190

Abstract

In the last decades, data-driven methods have gained great popularity in the industry, supported by state-of-the-art advancements in machine learning. These methods require a large quantity of labeled data, which is difficult to obtain and mostly costly and challenging. To address these challenges, [...] Read more.

In the last decades, data-driven methods have gained great popularity in the industry, supported by state-of-the-art advancements in machine learning. These methods require a large quantity of labeled data, which is difficult to obtain and mostly costly and challenging. To address these challenges, researchers have turned their attention to unsupervised and few-shot learning methods, which produced encouraging results, particularly in the areas of computer vision and natural language processing. With the lack of pretrained models, time series feature learning is still considered as an open area of research. This paper presents an efficient two-stage feature learning approach for anomaly detection in machine processes, based on a prototype few-shot learning technique that requires a limited number of labeled samples. The work is evaluated on a real-world scenario using the publicly available CNC Machining dataset. The proposed method outperforms the conventional prototypical network and the feature analysis shows a high generalization ability achieving an F1-score of 90.3%. The comparison with handcrafted features proves the robustness of the deep features and their invariance to data shifts across machines and time periods, which makes it a reliable method for sensory industrial applications. Full article

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25 pages, 1829 KB

Open AccessArticle

Degradation Detection in a Redundant Sensor Architecture

by Amer Kajmakovic, Konrad Diwold, Kay Römer, Jesus Pestana and Nermin Kajtazovic

Sensors 2022, 22(12), 4649; https://doi.org/10.3390/s22124649 - 20 Jun 2022

Cited by 11 | Viewed by 4976

Abstract

Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking [...] Read more.

Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down. Full article

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20 pages, 6127 KB

Open AccessArticle

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge

by Christos Spandonidis, Panayiotis Theodoropoulos and Fotis Giannopoulos

Sensors 2022, 22(11), 4105; https://doi.org/10.3390/s22114105 - 28 May 2022

Cited by 37 | Viewed by 5522

Abstract

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The [...] Read more.

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution. Full article

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13 pages, 2782 KB

Open AccessArticle

Acoustic Multi-Parameter Early Warning Method for Transformer DC Bias State

by Yuhao Zhou and Bowen Wang

Sensors 2022, 22(8), 2906; https://doi.org/10.3390/s22082906 - 10 Apr 2022

Cited by 4 | Viewed by 2116

Abstract

The acoustic signal in the operation of a power transformer contains a lot of transformer operation state information, which is of great significance to the detection of DC bias state. In this paper, three typical parameters used for DC bias state detection are [...] Read more.

The acoustic signal in the operation of a power transformer contains a lot of transformer operation state information, which is of great significance to the detection of DC bias state. In this paper, three typical parameters used for DC bias state detection are selected by comparing the acoustic variation of a 500 kV Jingting transformer substation No. 2 transformer with that of the core model built in the laboratory; then, acoustic samples of the 162 EHV normal state transformers are collected, and the distribution regularity of three typical parameters in normal state is given. Finally, according to the distribution regularity, clear warning threshold of typical parameters are given, and the DC bias cases from the 500 kV Jingting transformer substation are used to verify the effectiveness of the threshold. Full article

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15 pages, 383 KB

Open AccessArticle

MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach

by Milot Gashi, Heimo Gursch, Hannes Hinterbichler, Stefan Pichler, Stefanie Lindstaedt and Stefan Thalmann

Sensors 2022, 22(8), 2837; https://doi.org/10.3390/s22082837 - 7 Apr 2022

Cited by 7 | Viewed by 3608

Abstract

Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting [...] Read more.

Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting maintenance data is complex and challenging as it requires human involvement and expertise. Due to time constraints, motivating workers to provide comprehensive labeled data is very challenging, and thus maintenance data are mostly incomplete or even completely missing. In addition to these aspects, a lot of condition monitoring data-sets exist, but only very few labeled small maintenance data-sets can be found. Hence, our proposed solution can provide additional labels and offer new research possibilities for these data-sets. To address this challenge, we introduce MEDEP, a novel maintenance event detection framework based on the Pruned Exact Linear Time (PELT) approach, promising a low false-positive (FP) rate and high accuracy results in general. MEDEP could help to automatically detect performed maintenance events from the deviations in the condition monitoring data. A heuristic method is proposed as an extension to the PELT approach consisting of the following two steps: (1) mean threshold for multivariate time series and (2) distribution threshold analysis based on the complexity-invariant metric. We validate and compare MEDEP on the Microsoft Azure Predictive Maintenance data-set and data from a real-world use case in the welding industry. The experimental outcomes of the proposed approach resulted in a superior performance with an FP rate of around 10% on average and high sensitivity and accuracy results. Full article

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17 pages, 2344 KB

Open AccessArticle

A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems

by Xue Li, Zhikang Fan, Shengfeng Wang, Aibing Qiu and Jingfeng Mao

Sensors 2022, 22(7), 2480; https://doi.org/10.3390/s22072480 - 23 Mar 2022

Cited by 2 | Viewed by 3034

Abstract

This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will [...] Read more.

This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework. Full article

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22 pages, 7819 KB

Open AccessArticle

Batch Process Monitoring Based on Quality-Related Time-Batch 2D Evolution Information

by Luping Zhao and Jiayang Yang

Sensors 2022, 22(6), 2235; https://doi.org/10.3390/s22062235 - 14 Mar 2022

Cited by 3 | Viewed by 3548

Abstract

This paper proposed a quality-related online monitoring strategy based on time and batch two-dimensional evolution information for batch processes. In the direction of time, considering the difference between each phase and the steady part and the transition part in the same phase, the [...] Read more.

This paper proposed a quality-related online monitoring strategy based on time and batch two-dimensional evolution information for batch processes. In the direction of time, considering the difference between each phase and the steady part and the transition part in the same phase, the change trend of the regression coefficient of the PLS model is used to divide each batch into phases, and each phase into parts. The phases, the steady parts, and the transition parts are finally distinguished and dealt with separately in the subsequent modeling process. In the batch direction, considering the slow time-varying characteristics of batch evolution, sliding windows are used to perform mode division by analyzing the evolution trend of the score matrix T in the PLS model on the base of phase division and within-phase part division. Finally, an online monitoring model that comprehensively considers the evolution information of time and batch is obtained. In a typical batch operation process, injection molding is used as an example for experimental analysis. The results show that the proposed algorithm takes advantage of mixing the time-batch two-dimensional evolution information. Compared with the traditional methods, the proposed method can overcome the shortcomings caused by the single dimension analysis and has better monitoring results. Full article

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15 pages, 4133 KB

Open AccessArticle

Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach

by Dip Kumar Saha, Md. Emdadul Hoque and Hamed Badihi

Sensors 2022, 22(3), 1073; https://doi.org/10.3390/s22031073 - 29 Jan 2022

Cited by 39 | Viewed by 5367

Abstract

The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep [...] Read more.

The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms. Full article

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2021

Jump to: 2022

21 pages, 2813 KB

Open AccessArticle

A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

by Rafia Nishat Toma, Farzin Piltan and Jong-Myon Kim

Sensors 2021, 21(24), 8453; https://doi.org/10.3390/s21248453 - 18 Dec 2021

Cited by 41 | Viewed by 7475

Abstract

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features [...] Read more.

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal. Full article

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14 pages, 2132 KB

Open AccessArticle

Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

by Hui Yu, Chuang Chen, Ningyun Lu and Cunsong Wang

Sensors 2021, 21(24), 8373; https://doi.org/10.3390/s21248373 - 15 Dec 2021

Cited by 9 | Viewed by 3071

Abstract

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the [...] Read more.

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA’s open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs. Full article

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22 pages, 8149 KB

Open AccessArticle

Internal Damage Detection of Composite Structures Using Passive RFID Tag Antenna Deformation Method: Basic Research

by Pavol Pecho, Michal Hrúz, Andrej Novák and Libor Trško

Sensors 2021, 21(24), 8236; https://doi.org/10.3390/s21248236 - 9 Dec 2021

Cited by 7 | Viewed by 4689

Abstract

This manuscript deals with the detection of internal cracks and defects in aeronautical fibreglass structures. In technical practice, it is problematic to accurately determine the service life or MTBF (Mean Time Between Failure) of composite materials by the methods used in metallic materials. [...] Read more.

This manuscript deals with the detection of internal cracks and defects in aeronautical fibreglass structures. In technical practice, it is problematic to accurately determine the service life or MTBF (Mean Time Between Failure) of composite materials by the methods used in metallic materials. The problem is mainly the inhomogeneous and anisotropic structure of composites, possibly due to the differences in the macrostructure during production, production processes, etc. Diagnostic methods for detecting internal cracks and damage are slightly different, and in practice, it is more difficult to detect defects using non-destructive testing (NDT). The article deals with the use of Radio frequency identification (RFID) technology integrated in the fibreglass laminates of aircraft structures to detect internal defects based on deformation behaviour of passive RFID tag antenna. The experiments proved the potential of using RFID technology in fibreglass composite laminates when using tensile tests applied on specimens with different structural properties. Therefore, the implementation of passive RFID tags into fibreglass composite structures presents the possibilities of detecting internal cracks and structural health monitoring. The result and conclusion of the basic research is determination of the application conditions for our proposed technology in practice. Moreover, the basic research provides recommendations for the applied research in terms of the use in real composite airframe structures. Full article

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14 pages, 2182 KB

Open AccessArticle

The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults

by Tiago Drummond Lopes, Adroaldo Raizer and Wilson Valente Júnior

Sensors 2021, 21(23), 7833; https://doi.org/10.3390/s21237833 - 25 Nov 2021

Cited by 28 | Viewed by 5201

Abstract

Induction motors play a key role in the industrial sector. Thus, the correct diagnosis and classification of faults on these machines are important, even in the initial stages of evolution. Such analysis allows for increased productivity, avoids unexpected process interruptions, and prevents damage [...] Read more.

Induction motors play a key role in the industrial sector. Thus, the correct diagnosis and classification of faults on these machines are important, even in the initial stages of evolution. Such analysis allows for increased productivity, avoids unexpected process interruptions, and prevents damage to machines. Usually, fault diagnosis is carried out by analyzing the characteristic effects caused by the faults. Thus, it is necessary to know and understand the behavior during the operation of the faulty machine. In general, monitoring these characteristics is complex, as it is necessary to acquire signals from the same motor with and without failures for comparison purposes. Whether in an industrial environment or in laboratories, the experimental characterization of failures can become unfeasible for several reasons. Thus, computer simulation of faulty motors digital twins can be an important alternative for failure analysis, especially in large motors. From this perspective, this paper presents and discusses several limitations found in the technical literature that can be minimized with the implementation of digital twins. In addition, a 3D finite element model of an induction motor with broken rotor bars is demonstrated, and motor current signature analysis is used to verify the fault effects. Results are analyzed in the time and frequency domain. Additionally, an artificial neural network of the multilayer perceptron type is used to classify the failure of broken bars in the 3D model rotor. Full article

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14 pages, 3236 KB

Open AccessArticle

Broken Rotor Bar Detection in Induction Motors through Contrast Estimation

by Edna Rocio Ferrucho-Alvarez, Ana Laura Martinez-Herrera, Eduardo Cabal-Yepez, Carlos Rodriguez-Donate, Misael Lopez-Ramirez and Ruth Ivonne Mata-Chavez

Sensors 2021, 21(22), 7446; https://doi.org/10.3390/s21227446 - 9 Nov 2021

Cited by 24 | Viewed by 4530

Abstract

Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular [...] Read more.

Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors’ knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state. Full article

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29 pages, 4151 KB

Open AccessArticle

Reliable Route Selection for Wireless Sensor Networks with Connection Failure Uncertainties

by Jianhua Lyu, Yiran Ren, Zeeshan Abbas and Baili Zhang

Sensors 2021, 21(21), 7254; https://doi.org/10.3390/s21217254 - 31 Oct 2021

Cited by 3 | Viewed by 2885

Abstract

For wireless sensor networks (WSN) with connection failure uncertainties, traditional minimum spanning trees are no longer a feasible option for selecting routes. Reliability should come first before cost since no one wants a network that cannot work most of the time. First, reliable [...] Read more.

For wireless sensor networks (WSN) with connection failure uncertainties, traditional minimum spanning trees are no longer a feasible option for selecting routes. Reliability should come first before cost since no one wants a network that cannot work most of the time. First, reliable route selection for WSNs with connection failure uncertainties is formulated by considering the top-k most reliable spanning trees (RST) from graphs with structural uncertainties. The reliable spanning trees are defined as a set of spanning trees with top reliabilities and limited tree weights based on the possible world model. Second, two tree-filtering algorithms are proposed: the k minimum spanning tree (KMST) based tree-filtering algorithm and the depth-first search (DFS) based tree-filtering algorithm. Tree-filtering strategy filters the candidate RSTs generated by tree enumeration with explicit weight thresholds and implicit reliability thresholds. Third, an innovative edge-filtering method is presented in which edge combinations that act as upper bounds for RST reliabilities are utilized to filter the RST candidates and to prune search spaces. Optimization strategies are also proposed for improving pruning capabilities further and for enhancing computations. Extensive experiments are conducted to show the effectiveness and efficiency of the proposed algorithms. Full article

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19 pages, 545 KB

Open AccessArticle

A Fault Diagnosis Method of Modular Analog Circuit Based on SVDD and D–S Evidence Theory

by Peng Sun, Zhiming Yang, Yueming Jiang, Shaohua Jia and Xiyuan Peng

Sensors 2021, 21(20), 6889; https://doi.org/10.3390/s21206889 - 18 Oct 2021

Cited by 14 | Viewed by 3593

Abstract

In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became [...] Read more.

In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became a research hotspot in the field of analog circuit fault diagnosis. This paper proposes a method based on Support Vector Data Description (SVDD) and Dempster–Shafer evidence theory (D–S evidence theory) for fault diagnosis of modular analog circuit. Firstly, the principle of circuit module partition is proposed to divide the analog circuit under test, and the output port of each module is selected as test point. Secondly, the paper extracts the feature of the time-domain and frequency-domain output signals of the circuit module through Principal Component Analysis (PCA). Thirdly, four state detection models based on SVDD are established to judge the working state of each circuit module, including TSG, TSP, FSG, and FSP state detection model. Finally, the D–S theory is introduced to integrate the test results of each model for locating fault circuit module. To verify the effectiveness of the proposed method, the dual bandpass filter circuit is selected for simulation and hardware experiment. The results show that the proposed method can locate the analog fault effectively and has a higher diagnosis accuracy. Full article

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17 pages, 1384 KB

Open AccessArticle

Identification of Distributed Denial of Services Anomalies by Using Combination of Entropy and Sequential Probabilities Ratio Test Methods

by Basheer Husham Ali, Nasri Sulaiman, Syed Abdul Rahman Al-Haddad, Rodziah Atan, Siti Lailatul Mohd Hassan and Mokhalad Alghrairi

Sensors 2021, 21(19), 6453; https://doi.org/10.3390/s21196453 - 27 Sep 2021

Cited by 13 | Viewed by 3097

Abstract

One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished [...] Read more.

One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing through. Entropy (E) is the first technique, and the sequential probabilities ratio test (SPRT) is the second technique. The entropy method alone compares its results with a certain threshold in order to make a decision. The accuracy and F-scores for entropy results thus changed when the threshold values changed. Using both entropy and SPRT removed the uncertainty associated with the entropy threshold. The false positive rate was also reduced when combining both techniques. Entropy-based detection methods divide incoming traffic into groups of traffic that have the same size. The size of these groups is determined by a parameter called window size. The Defense Advanced Research Projects Agency (DARPA) 1998, DARPA2000, and Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were used to evaluate the implementation of this method. The metric of a confusion matrix was used to compare the ESPRT results with the results of other methods. The accuracy and f-scores for the DARPA 1998 dataset were 0.995 and 0.997, respectively, for the ESPRT method when the window size was set at 50 and 75 packets. The detection rate of ESPRT for the same dataset was 0.995 when the window size was set to 10 packets. The average accuracy for the DARPA 2000 dataset for ESPRT was 0.905, and the detection rate was 0.929. Finally, ESPRT was scalable to a multiple domain topology application. Full article

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17 pages, 900 KB

Open AccessArticle

ConAnomaly: Content-Based Anomaly Detection for System Logs

by Dan Lv, Nurbol Luktarhan and Yiyong Chen

Sensors 2021, 21(18), 6125; https://doi.org/10.3390/s21186125 - 13 Sep 2021

Cited by 17 | Viewed by 4957

Abstract

Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log parser to get log event indexes or event [...] Read more.

Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log parser to get log event indexes or event templates and then utilize machine learning methods to detect anomalies. However, these methods cannot handle unknown log types and do not take advantage of the log semantic information. In this article, we propose ConAnomaly, a log-based anomaly detection model composed of a log sequence encoder (log2vec) and multi-layer Long Short Term Memory Network (LSTM). We designed log2vec based on the Word2vec model, which first vectorized the words in the log content, then deleted the invalid words through part of speech tagging, and finally obtained the sequence vector by the weighted average method. In this way, ConAnomaly not only captures semantic information in the log but also leverages log sequential relationships. We evaluate our proposed approach on two log datasets. Our experimental results show that ConAnomaly has good stability and can deal with unseen log types to a certain extent, and it provides better performance than most log-based anomaly detection methods. Full article

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17 pages, 3987 KB

Open AccessArticle

Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing

by Taeyun Kim and Jangbom Chai

Sensors 2021, 21(15), 4970; https://doi.org/10.3390/s21154970 - 21 Jul 2021

Cited by 14 | Viewed by 3987

Abstract

Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by [...] Read more.

Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotating machines, so many artificial intelligent models have been developed to diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating conditions such as experimental environment, number of balls, degree of defects, and rotational speed. Cross-domain fault diagnosis has been successfully performed when the systems are the same but operating conditions are different. However, the results are poor when diagnosing different bearing systems because the characteristics of the signals such as specific frequencies depend on the specifications. In this paper, the pre-processing method was used for improving the diagnosis without prior knowledge such as fault frequencies. The signals were first transformed to a common pattern space before entering the models. To develop and to validate the proposed method for different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve University datasets and Paderborn University datasets) were used. One dimensional CNN models were utilized for verification of the proposed method and the results of the models using raw datasets and pre-processed datasets were compared. Even though each of the ball-bearing systems have their own specifications, using the proposed method was very helpful for domain adaptation, and cross-domain fault diagnosis was performed with high accuracy. Full article

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24 pages, 8101 KB

Open AccessArticle

Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

by Yongze Jin, Guo Xie, Yankai Li, Xiaohui Zhang, Ning Han, Anqi Shangguan and Wenbin Chen

Sensors 2021, 21(13), 4370; https://doi.org/10.3390/s21134370 - 25 Jun 2021

Cited by 17 | Viewed by 3843

Abstract

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation [...] Read more.

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified. Full article

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