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Intelligent Systems for Fault Diagnosis and Prognosis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 32620

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Special Issue Editors

Dr. Yong Oh Lee

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Guest Editor

Korea Institute of Science and Technology Europe FmbH , Campus E7 1, 66123 Saarbrücken, Germany
Interests: machine learning; deep learning; generative adversarial networks; intelligent systems; data-driven models; prognostics and health management; bearing fault prediction; image processing

Dr. Mayank Jha

E-Mail Website
Guest Editor

Research Centre for Automatic Control in Nancy, University of Lorraine, 34 Cours Léopold, 54000 Nancy, France
Interests: optimal control; adaptive/approximate dynamic programming for optimal control; reinforcement learning for cyber physical systems; prognostics of systems using deep learning

Special Issue Information

Dear Colleagues,

Prognostics and health management (PHM) has become one of the most sought-after areas in the domain of predictive maintenance, involving various technologies that collect status information from industrial systems, such as manufacturing machines, facilities, and power plants, to detect failures of the system and enable maintenance scheduling in advance by predicting the point of failure through predictive analysis and verification. Recently, data-driven approaches using machine learning and deep learning have achieved remarkable improvements, especially in the face of unknown non-linear machine behaviors and non-stationary sensor data.

However, more efficient and robust methods are required for real deployment in industry. Moreover, the availability of limited asset datasets, such as degradation data, poses greater challenges and calls for novel methods for efficient and rapid learning under limited data/sensing.

The Special Issue “Intelligent Systems for Fault Diagnosis and Prognosis” aims to collect the state of the art of research in data-driven diagnosis and prognosis for machine faults, ranging from fundamental studies to practical applications. The Special Issue will include, but is not limited to, the following topics:

Sensors and data collection;
Data quality management and data processing;
Feature extraction and fault classification for condition monitoring;
Machine learning and deep learning algorithms;
Transfer-learning-based methods for prognostics;
Reinforcement-learning-based methods for predictive health management;
Hybrid models of data-driven and model-based approaches;
Uncertainty analysis for prognostics;
Novel system structures for predictive maintenance;
Other PHM applications.

Dr. Yong Oh Lee
Dr. Mayank JHA
Guest Editors

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Published Papers (14 papers)

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Research

19 pages, 3774 KiB

Open AccessArticle

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

by Haemi Lee, Yoonjae Lee, Minho Jo, Sanghoon Nam, Jeongdai Jo and Changwoo Lee

Sensors 2023, 23(18), 7857; https://doi.org/10.3390/s23187857 - 13 Sep 2023

Viewed by 1069

Abstract

Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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19 pages, 3936 KiB

Open AccessArticle

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators

by Ervin Galan-Uribe, Juan P. Amezquita-Sanchez and Luis Morales-Velazquez

Sensors 2023, 23(6), 3213; https://doi.org/10.3390/s23063213 - 17 Mar 2023

Cited by 2 | Viewed by 2012

Abstract

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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11 pages, 4718 KiB

Open AccessArticle

Micro-Three-Coil Sensor with Dual Excitation Signals Use Asymmetric Magnetic Fields to Distinguish between Non-Ferrous Metals

by Jiaju Hong, Yucai Xie, Shuyao Zhang, Haotian Shi, Yu Liu, Hongpeng Zhang and Yuqing Sun

Sensors 2023, 23(3), 1637; https://doi.org/10.3390/s23031637 - 02 Feb 2023

Cited by 1 | Viewed by 1511

Abstract

Intelligent operation and maintenance technology for vessels can ensure the safety of the entire system, especially for the development of intelligent and unmanned marine technology. The material properties of metal abrasive particles in oil could demonstrate the wear areas of the marine mechanical system because different components consist of different materials. However, most sensors can only roughly separate metallic contaminants into ferromagnetic and non-ferromagnetic particles but cannot differentiate them in greater detail. A micro-three-coil sensor is designed in this paper; the device applies different excitation signals to two excitation coils to differentiate materials, based on the different effects of different material particles in the asymmetric magnetic field. Therefore, a particle’s material can be judged by the shape of the induction electromotive force output signal from the induction coil, while the particle size can be judged by the amplitude of the signal. Experimental results show that the material differentiation of four different types of particles can be achieved, namely, of aluminum, iron, 304 stainless steel, and carbon steel. This newly designed sensor provides a new research prospect for the realization of an inductive detection method to distinguish non-ferrous metals and a reference for the subsequent detection of metal contaminants in oil and other liquids. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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22 pages, 3992 KiB

Open AccessArticle

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

by Jae Seok Do, Akeem Bayo Kareem and Jang-Wook Hur

Sensors 2023, 23(2), 1009; https://doi.org/10.3390/s23021009 - 15 Jan 2023

Cited by 13 | Viewed by 4597

Abstract

Industry 5.0, also known as the “smart factory”, is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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23 pages, 5213 KiB

Open AccessArticle

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

by Tiago Gaspar da Rosa, Arthur Henrique de Andrade Melani, Fabio Henrique Pereira, Fabio Norikazu Kashiwagi, Gilberto Francisco Martha de Souza and Gisele Maria De Oliveira Salles

Sensors 2022, 22(24), 9738; https://doi.org/10.3390/s22249738 - 12 Dec 2022

Cited by 1 | Viewed by 1403

Abstract

This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems’ safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growth to signalize a fault’s occurrence while individually evaluating each monitored variable to provide fault detection and prognosis. Additionally, the paper also provides an appropriate set of metrics to measure the accuracy of the models, which is a common disadvantage of unsupervised methods due to the lack of predefined answers during training. Computational results using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) monitoring data show the effectiveness of the proposed framework. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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28 pages, 17106 KiB

Open AccessArticle

Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

by Przemyslaw Pietrzak and Marcin Wolkiewicz

Sensors 2022, 22(24), 9668; https://doi.org/10.3390/s22249668 - 10 Dec 2022

Cited by 6 | Viewed by 1780

Abstract

Permanent magnet synchronous motors (PMSMs) have become one of the most important components of modern drive systems. Therefore, fault diagnosis and condition monitoring of these machines have been the subject of many studies in recent years. This article presents an intelligent stator current-data driven PMSM stator winding fault detection and classification method. Short-time Fourier transform is applied in the process of fault feature extraction from the stator phase current symmetrical components signal. Automation of the fault detection and classification process is carried out with the use of three selected machine learning algorithms: support vector machine, naïve Bayes classifier and multilayer perceptron. The concept and online verification of the original intelligent fault diagnosis system with the potential of a real industrial deployment are demonstrated. Experimental results are presented to evaluate the effectiveness of the proposed methodology. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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21 pages, 5549 KiB

Open AccessArticle

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions

by Wangpeng He, Jing Chen, Yue Zhou, Xuan Liu, Binqiang Chen and Baolong Guo

Sensors 2022, 22(23), 9175; https://doi.org/10.3390/s22239175 - 25 Nov 2022

Cited by 10 | Viewed by 1851

Abstract

Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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16 pages, 4104 KiB

Open AccessArticle

A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network

by Xu Yang, Rui Yuan, Yong Lv, Li Li and Hao Song

Sensors 2022, 22(21), 8343; https://doi.org/10.3390/s22218343 - 30 Oct 2022

Cited by 6 | Viewed by 1768

Abstract

Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the dynamical information of tool wear conditions. This paper proposes a novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network (1D CNN). Firstly, multivariate variational mode decomposition (MVMD) is used to process the multivariate cutting force signals. The multivariate band-limited intrinsic mode functions (BLIMFs) are obtained, which contain a large number of nonlinear and nonstationary tool wear characteristics. Afterwards, the proposed modified multiscale permutation entropy (MMPE) is used to measure the complexity of multivariate BLIMFs. The entropy values on multiple scales are calculated as condition indicators in tool wear condition monitoring. Finally, the one-dimensional feature vectors are constructed and employed as the input of 1D CNN to achieve accurate and stable tool wear condition monitoring. The results of the research in this paper demonstrate that the proposed approach has broad prospects in tool wear condition monitoring. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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22 pages, 2324 KiB

Open AccessArticle

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

by Adam M. Terwilliger and Joshua E. Siegel

Sensors 2022, 22(20), 7736; https://doi.org/10.3390/s22207736 - 12 Oct 2022

Cited by 1 | Viewed by 1503

Abstract

In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance misfire fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio. Through cascading fuel type, engine configuration, cylinder count and aspiration type attributes, our cascading CNN achieves 87.0% test set accuracy on misfire fault detection which demonstrates margins of 8.0% and 1.7% over naïve and parallel CNN baselines. We explore experimental studies focused on acoustic features, data augmentation, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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20 pages, 7042 KiB

Open AccessArticle

Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries

by Jong-Hyun Lee and In-Soo Lee

Sensors 2022, 22(15), 5536; https://doi.org/10.3390/s22155536 - 25 Jul 2022

Cited by 7 | Viewed by 2141

Abstract

Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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28 pages, 7718 KiB

Open AccessArticle

Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

by Farzin Piltan, Rafia Nishat Toma, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo and Jong-Myon Kim

Sensors 2022, 22(2), 539; https://doi.org/10.3390/s22020539 - 11 Jan 2022

Cited by 13 | Viewed by 2073

Abstract

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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13 pages, 4951 KiB

Open AccessArticle

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

by Akhand Rai, Zahoor Ahmad, Md Junayed Hasan and Jong-Myon Kim

Sensors 2021, 21(24), 8247; https://doi.org/10.3390/s21248247 - 10 Dec 2021

Cited by 9 | Viewed by 3598

Abstract

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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15 pages, 5535 KiB

Open AccessArticle

A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

by Bin Han, Hui Zhang, Ming Sun and Fengtong Wu

Sensors 2021, 21(22), 7762; https://doi.org/10.3390/s21227762 - 22 Nov 2021

Cited by 31 | Viewed by 2893

Abstract

Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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23 pages, 6643 KiB

Open AccessArticle

Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

by Weiwei Zhang, Deji Chen and Yang Kong

Sensors 2021, 21(14), 4774; https://doi.org/10.3390/s21144774 - 13 Jul 2021

Cited by 9 | Viewed by 2583

Abstract

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods. Full article

(This article belongs to the Special Issue Intelligent Systems for Fault Diagnosis and Prognosis)

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