Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Automation automation	-	2.9	2020	20.6 Days	CHF 1000	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Machines machines	2.1	3.0	2013	15.6 Days	CHF 2400	Submit
Symmetry symmetry	2.2	5.4	2009	16.8 Days	CHF 2400	Submit

21 pages, 4153 KiB

Open AccessArticle

Enhancing Fault Diagnosis in IoT Sensor Data through Advanced Preprocessing Techniques

by Sang-Ha Sung, Soongoo Hong, Hyung-Rim Choi, Do-Myung Park and Sangjin Kim

Electronics 2024, 13(16), 3289; https://doi.org/10.3390/electronics13163289 - 19 Aug 2024

Viewed by 367

Through innovation in the data collection environment, data-driven fault diagnosis has become increasingly important. This study aims to develop an algorithm to improve the accuracy of fault diagnosis based on Internet of Things (IoT) sensor data. In this research, current data collected through [...] Read more.

Through innovation in the data collection environment, data-driven fault diagnosis has become increasingly important. This study aims to develop an algorithm to improve the accuracy of fault diagnosis based on Internet of Things (IoT) sensor data. In this research, current data collected through IoT sensors is utilized, focusing on diagnosing four states: bearing defects, shaft misalignment, rotor imbalance, and belt looseness. Additionally, to enhance the efficiency of the fault diagnosis algorithm, we introduce a preprocessing technique that utilizes descriptive statistics to reduce the data dimensionality. The experiments are conducted based on current data and vibration data, ensuring reliability from both types of data. The experimental results indicate a significant improvement in the accuracy and computational time of the fault diagnosis algorithm. After experimenting with various candidate algorithms, XGBoost version 1.7.6 exhibited the highest performance of classification. This research contributes to enhancing safety and reliability based on IoT sensors and suggests potential applications in the field of fault diagnosis. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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22 pages, 3445 KiB

Open AccessArticle

An Intelligent Power Transformers Diagnostic System Based on Hierarchical Radial Basis Functions Improved by Linde Buzo Gray and Single-Layer Perceptron Algorithms

by Mounia Hendel, Imen Souhila Bousmaha, Fethi Meghnefi, Issouf Fofana and Mostefa Brahami

Energies 2024, 17(13), 3171; https://doi.org/10.3390/en17133171 - 27 Jun 2024

Viewed by 607

Abstract

Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost [...] Read more.

Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost reduction, and rapid response time—dissolved gas analysis (DGA) is regarded as one of the most effective ways to assess a transformer’s condition. In this contribution, we propose a new probabilistic hierarchical intelligent system consisting of five subnetworks of the radial basis functions (RBF) type. Indeed, hierarchical classification minimizes the complexity of the discrimination task by employing a divide-and-conquer strategy, effectively addressing the issue of unbalanced data (a significant disparity between the categories to be predicted). This approach contributes to a more precise and sophisticated diagnosis of transformers. The first subnetwork detects the presence or absence of defects, separating defective samples from healthy ones. The second subnetwork further classifies the defective samples into three categories: electrical, thermal, and cellulosic decomposition. The samples in these categories are then precisely assigned to their respective subcategories by the third, fourth, and fifth subnetworks. To optimize the hyperparameters of the five models, the Linde–Buzo–Gray algorithm is implemented to reduce the number of centers (radial functions) in each subnetwork. Subsequently, a single-layer perceptron is trained to determine the optimal synaptic weights, which connect the intermediate layer to the output layer. The results obtained with our proposed system surpass those achieved with another implemented alternative (a single RBF), with an average sensitivity percentage as high as 96.85%. This superiority is validated by a Student’s t-test, showing a significant difference greater than 5% (p-value < 0.001). These findings demonstrate and highlight the relevance of the proposed hierarchical configuration. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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21 pages, 7115 KiB

Open AccessArticle

Failure Mechanism Information-Assisted Multi-Domain Adversarial Transfer Fault Diagnosis Model for Rolling Bearings under Variable Operating Conditions

by Zhidan Zhong, Zhihui Zhang, Yunhao Cui, Xinghui Xie and Wenlu Hao

Electronics 2024, 13(11), 2133; https://doi.org/10.3390/electronics13112133 - 30 May 2024

Viewed by 652

Abstract

Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in [...] Read more.

Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in the case of variable working conditions or lack of annotated data for the target domain. This may lead to unstable training results or negative transfer of the neural network. This paper proposes a new method for enhancing unsupervised domain adaptation in bearing fault diagnosis, aimed at providing robust fault diagnosis for rolling bearings under varying operating conditions. It incorporates bearing fault finite element simulation data into the domain adversarial network, guiding adversarial training using fault evolution mechanisms. The algorithm establishes global and subdomain classifiers, with simulation signals replacing label predictions for target data in the subdomain, ensuring minimal information transfer. By reconstructing the loss function, we can extract the common features of the same type bearing under different conditions and enhance the domain antagonism robustness. The proposed method is validated using two sets of testbed data as target domains. The results demonstrate that the method can adequately adapt the deep feature distributions of the model and experimental domains, thereby improving the accuracy of fault diagnosis in unsupervised cross-domain scenarios. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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15 pages, 2752 KiB

Open AccessArticle

An Energy-Efficient Field-Programmable Gate Array Rapid Implementation of a Structural Health Monitoring System

by Maciej Rosół and Wojciech Kula

Energies 2024, 17(11), 2626; https://doi.org/10.3390/en17112626 - 29 May 2024

Viewed by 594

Abstract

System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. [...] Read more.

System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during calculations is the lowest for the Kria platform using the FPGA system. However, the best ratio of the average power consumption to the accuracy of the neural network was obtained for the Raspberry Pi 4B. The concept of an efficient and energy-saving hardware platform that enables monitoring and analysis of the operation of the selected dynamic system was proposed. It allows for easy integration of many software environments (e.g., MATLAB and Python) with the System-on-a-Chip (SoC) platform containing an FPGA and a CPU. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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22 pages, 6841 KiB

Open AccessArticle

A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory

by Yong Liu, Jiaqi Liu, Han Wang, Mingshun Yang, Xinqin Gao and Shujuan Li

Machines 2024, 12(5), 342; https://doi.org/10.3390/machines12050342 - 15 May 2024

Viewed by 692

Abstract

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components [...] Read more.

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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20 pages, 8099 KiB

Open AccessArticle

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

by Yong Liu, Zhuo Wang, Dong Zhang, Mingshun Yang, Xinqin Gao and Li Ba

Symmetry 2024, 16(5), 532; https://doi.org/10.3390/sym16050532 - 29 Apr 2024

Viewed by 717

Abstract

Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data [...] Read more.

Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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25 pages, 12847 KiB

Open AccessArticle

Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks

by Dominik Łuczak

Energies 2024, 17(9), 1998; https://doi.org/10.3390/en17091998 - 23 Apr 2024

Cited by 4 | Viewed by 972

Abstract

Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are [...] Read more.

Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are proposed. The IMU6DoF-Time2GrayscaleGrid-CNN method converts the time series sensor data into a single grayscale image, leveraging the efficiency of a grayscale representation and the power of convolutional neural networks (CNNs) for feature extraction. The IMU6DoF-Time2RGBbyType-CNN method utilizes RGB images. The IMU6DoF-Time2RGBbyAxis-CNN method employs an RGB image where each channel corresponds to a specific axis (X, Y, Z) of the sensor data. This axis-aligned representation potentially allows the CNN to learn the relationships between movements along different axes. The performance of all three methods is evaluated through extensive training and testing on a dataset containing various operational states (idle, normal, fault). All methods achieve high accuracy in classifying these states. While the grayscale method offers the fastest training convergence, the RGB-based methods might provide additional insights. The interpretability of the models is also explored using Grad-CAM visualizations. This research demonstrates the potential of image-based approaches with CNNs for robust and interpretable machine fault diagnosis using sensor data. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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16 pages, 3077 KiB

Open AccessArticle

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

by Zhongqi Guo, Xiu Ji, Hui Wang and Xiao Yang

Electronics 2024, 13(7), 1202; https://doi.org/10.3390/electronics13071202 - 25 Mar 2024

Cited by 1 | Viewed by 691

Abstract

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction [...] Read more.

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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28 pages, 12037 KiB

Open AccessArticle

Improved Generative Adversarial Network for Bearing Fault Diagnosis with a Small Number of Data and Unbalanced Data

by Zhaohui Qin, Faguo Huang, Jiafang Pan, Junlin Niu and Haihua Qin

Symmetry 2024, 16(3), 358; https://doi.org/10.3390/sym16030358 - 15 Mar 2024

Cited by 2 | Viewed by 1293

Abstract

Fault data under real operating conditions are often difficult to collect, making the number of trained fault data small and out of proportion to normal data. Thus, fault diagnosis symmetry (balance) is compromised. This will result in less effective fault diagnosis methods for [...] Read more.

Fault data under real operating conditions are often difficult to collect, making the number of trained fault data small and out of proportion to normal data. Thus, fault diagnosis symmetry (balance) is compromised. This will result in less effective fault diagnosis methods for cases with a small number of data and data imbalances (S&I). We present an innovative solution to overcome this problem, which is composed of two components: data augmentation and fault diagnosis. In the data augmentation section, the S&I dataset is supplemented with a deep convolutional generative adversarial network based on a gradient penalty and Wasserstein distance (WDCGAN-GP), which solve the problems of the generative adversarial network (GAN) being prone to model collapse and the gradient vanishing during the training time. The addition of self-attention allows for a better identification and generation of sample features. Finally, the addition of spectral normalization can stabilize the training of the model. In the fault diagnosis section, fault diagnosis is performed through a convolutional neural network with coordinate attention (CNN-CA). Our experiments conducted on two bearing fault datasets for comparison demonstrate that the proposed method surpasses other comparative approaches in terms of the quality of data augmentation and the accuracy of fault diagnosis. It effectively addresses S&I fault diagnosis challenges. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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11 pages, 4219 KiB

Open AccessProject Report

Tool Wear Monitoring System Using Seq2Seq

by Wang-Su Jeon and Sang-Yong Rhee

Machines 2024, 12(3), 169; https://doi.org/10.3390/machines12030169 - 1 Mar 2024

Cited by 2 | Viewed by 1207

Abstract

The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes [...] Read more.

The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using

R^{2}

, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an

R^{2}

of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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16 pages, 11415 KiB

Open AccessArticle

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

by Xue Wang and Yu Zhao

Energies 2024, 17(4), 954; https://doi.org/10.3390/en17040954 - 19 Feb 2024

Cited by 1 | Viewed by 879

Abstract

In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the [...] Read more.

In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model’s diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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16 pages, 7362 KiB

Open AccessArticle

A Multi-Input Convolutional Neural Network Model for Electric Motor Mechanical Fault Classification Using Multiple Image Transformation and Merging Methods

by Insu Bae and Suan Lee

Machines 2024, 12(2), 105; https://doi.org/10.3390/machines12020105 - 2 Feb 2024

Cited by 1 | Viewed by 1717

Abstract

This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in [...] Read more.

This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in vibrations or currents. Our research focuses on enhancing the accuracy of fault classification in electric motor facilities, employing innovative image transformation methods—recurrence plots (RPs), the Gramian angular summation field (GASF), and the Gramian angular difference field (GADF)—in conjunction with a multi-input convolutional neural network (CNN) model. We conducted comprehensive experiments using datasets encompassing four types of machinery components: bearings, belts, shafts, and rotors. The results reveal that our multi-input CNN model exhibits exceptional performance in fault classification across all machinery types, significantly outperforming traditional single-input models. This study not only demonstrates the efficacy of advanced image transformation techniques in fault detection but also underscores the potential of multi-input CNN models in industrial fault diagnosis, paving the way for more reliable and efficient monitoring of electric motor machinery. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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