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Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 27719

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Prof. Dr. Heung Soo Kim

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Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 780-714, Republic of Korea
Interests: smart structures; laminated composites
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Salman Khalid

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Guest Editor

Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul 780-714, Republic of Korea
Interests: prognostics and health management (PHM); health and usage monitoring system (HUMS); artificial intelligence; electrical drives; instrumentation; HVAC; energy optimization; smart factory

Dr. Ananda Shankar

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Guest Editor

Indian School of Mines, Indian Institute of Technology, Dhanbad 600036, India
Interests: prognostics and health management (PHM); health and usage monitoring system (HUMS); artificial intelligence; electrical drives; instrumentation; HVAC; energy optimization; smart factory

Dr. Prashant Kumar

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Guest Editor

Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul 780-714, Republic of Korea
Interests: prognostics and health management (PHM); health and usage monitoring system (HUMS); artificial intelligence; electrical drives; electric vehicles; power electronics; HVAC; energy optimization; smart factory

Special Issue Information

Dear Colleagues,

Machines and mechanical structures undergo various faults during operation. The timely diagnosis of these faults and the prediction of their future health condition is essential for industrial productivity and reliability. Recently, intelligent fault diagnosis (IFD) has attracted much attention due to its promising ability to automatically recognize the health state of machines. Intelligent fault diagnosis (IFD) refers to applications of machine learning theories, such as artificial neural networks (ANN), support vector machine (SVM), and deep neural networks (DNN), to machine fault diagnosis. In the past, traditional machine learning (ML) theories began to reduce the contribution of human labor and brought forth the era of artificial intelligence to machine fault diagnosis. In recent years, the advent of deep learning (DL) theories has reformed IFD by further releasing artificial assistance that encouraged the development of an end-to-end diagnosis process.

The purpose of this Special Issue is to provide a research-publishing environment for articles with the latest developments in ML and DL approaches for real-world applications in intelligent fault diagnosis. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to:

Diagnostic and prognostic techniques based on AI;
Data-driven and model-based sensor fault diagnosis;
Feature construction with intelligent algorithms;
Data augmentation techniques for fault diagnosis;
AI-based solutions that are explainable;
Machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0.

We also welcome review articles that capture the current state of the art and outline future areas of research in the fields relevant to this Special Issue.

Prof. Dr. Heung Soo Kim
Prof. Dr. Salman Khalid
Dr. Ananda Shankar
Dr. Prashant Kumar
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

industrial systems
smart industry
fault diagnosis
deep neural networks
convolutional neural networks
intelligent machines
feature extraction and analysis
machine learning and deep learning algorithms
classification and clustering
pattern recognition
probabilistic and statistical methods

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Published Papers (11 papers)

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Research

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22 pages, 4452 KiB

Open AccessArticle

An Interpretable Time Series Forecasting Model for Predicting NOx Emission Concentration in Ferroalloy Electric Arc Furnace Plants

by Youngjin Seol, Seunghyun Lee, Jiho Lee, Chang-Wan Kim, Hyun Su Bak, Youngchul Byun and Janghyeok Yoon

Mathematics 2024, 12(6), 878; https://doi.org/10.3390/math12060878 - 16 Mar 2024

Viewed by 1324

Abstract

Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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13 pages, 3811 KiB

Open AccessArticle

Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process

by Nak-Hun Choi, Jung Woo Sohn and Jong-Seok Oh

Mathematics 2023, 11(24), 4894; https://doi.org/10.3390/math11244894 - 7 Dec 2023

Cited by 1 | Viewed by 2006

Abstract

In the manufacturing industry, which is facing the 4th Industrial Revolution, various process data are being collected from various sensors, and efforts are being made to construct more efficient processes using these data. Many studies have demonstrated high accuracy in predicting defect rates through image data collected during the process using two-dimensional (2D) convolutional neural network (CNN) algorithms, which are effective in image analysis. However, in an environment where numerous process data are recorded as numerical values, the application of 2D CNN algorithms is limited. Thus, to perform defect prediction through the application of a 2D CNN algorithm in a process wherein image data cannot be collected, this study attempted to develop a defect prediction technique that can visualize the data collected in numerical form. The polyurethane foam manufacturing process was selected as a case study to verify the proposed method, which confirmed that the defect rate could be predicted with an average accuracy of 97.32%. Consequently, highly accurate defect rate prediction and verification of the basis of judgment can be facilitated in environments wherein image data cannot be collected, rendering the proposed technique applicable to processes other than those in this case study. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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18 pages, 426 KiB

Open AccessArticle

Accuracy Is Not Enough: Optimizing for a Fault Detection Delay

by Matej Šprogar and Domen Verber

Mathematics 2023, 11(15), 3369; https://doi.org/10.3390/math11153369 - 1 Aug 2023

Viewed by 1115

Abstract

This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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25 pages, 7233 KiB

Open AccessArticle

Anomaly Detection of Underground Transmission-Line through Multiscale Mask DCNN and Image Strengthening

by Min-Gwan Kim, Siheon Jeong, Seok-Tae Kim and Ki-Yong Oh

Mathematics 2023, 11(14), 3143; https://doi.org/10.3390/math11143143 - 17 Jul 2023

Cited by 2 | Viewed by 1590

Abstract

This study proposes an integrated framework to automatically detect anomalies and faults in underground transmission-line connectors (UTLCs) with thermal images because anomaly detection of underground transmission-line connectors (UTLCs) plays a critical role in power line risk management. The proposed framework features three key characteristics. First, the measured thermal images were preprocessed through z-score normalization and image strengthening. Z-score normalization improves the robustness of feature extraction for UTLCs even though noise exists in a thermal image, and image strengthening improves the accuracy of segmentation for UTLCs. Second, a preprocessed thermal image is segmented to detect UTLCs by addressing a multiscale mask deep convolutional neural network (MS mask DCNN). The MS mask DCNN effectively detects UTLCs, enabling anomaly detection only for pixels of UTLCs. Specifically, the multiscale feature extraction module enables the extraction of distinct features of UTLCs and environments, and the skip-layer fusion module concatenates distinct features from the feature extraction module. Furthermore, a half tensor is used to reduce computational resources but maintain the same segmentation accuracy, enhancing the feasibility of the proposed framework in field applications. Third, anomaly detection is performed by addressing the contour method and unsupervised clustering method of DBSCAN. The contour method compensates for the limits of the MS mask DCNN for real-world applications because the neural networks cannot secure perfect accuracy of 100% owing to a lack of sufficient training images and low computational resources. DBSCAN improves the accuracy of diagnosis and ensures robustness to eliminate noise from thermal reflection caused by low-emissivity objects. Field experiments with high-voltage UTLCs demonstrated the effectiveness of the proposed framework. Ablation studies also confirmed that the methods addressed in this study outperform other methods. The proposed framework with a novel automatic non-destructive patrol inspection system would decrease the risks of human casualties during the periodic operation and maintenance of UTLCs, which are currently the most critical concerns. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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19 pages, 5141 KiB

Open AccessArticle

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

by Fengyun Xie, Gan Wang, Jiandong Shang, Enguang Sun and Sanmao Xie

Mathematics 2023, 11(12), 2679; https://doi.org/10.3390/math11122679 - 13 Jun 2023

Cited by 3 | Viewed by 1456

Abstract

The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method based on multi-sensor deep spatiotemporal feature representation. This method utilizes two vibration sensors to obtain the vibration information of the gearbox. A fault diagnosis model (PCNN–GRU) combined with a parallel convolutional neural network (PCNN) and gated recurrent unit (GRU) was used to fuse the gearbox vibration information. The parallel convolutional neural network was used to extract the spatial information of the vibration signals collected by different position sensors, and the timing information was mined through the gated recurrent unit. The deep spatiotemporal features that fuse the multi-sensor spatial and temporal information were composed. The collected multi-sensor vibration signals were directly input into the PCNN–GRU model, and an end-to-end intelligent diagnosis of the gearbox faults was realized. Finally, through experimental verification, the accuracy rate of this model can reach up to 99.92%. Compared with other models, this model has a higher diagnostic accuracy and stability. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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21 pages, 30146 KiB

Open AccessArticle

Application of Hybrid Model between the Technique for Order of Preference by Similarity to Ideal Solution and Feature Extractions for Bearing Defect Classification

by Chun-Yao Lee, Truong-An Le and Chung-Yao Chang

Mathematics 2023, 11(6), 1442; https://doi.org/10.3390/math11061442 - 16 Mar 2023

Cited by 1 | Viewed by 1398

Abstract

This paper describes a development that offers new opportunities for detecting faulty bearings. Prioritization is based on the technique for order of preference by similarity to the ideal solution (TOPSIS) for the most discriminative features in the faulty bearing dataset. The proposed model is divided into three steps: feature extraction, feature selection, and classification. In feature extraction, variational mode decomposition (VMD) and fast Fourier transform (FFT) are used to extract features from the measured signal of the test motors and use the symmetrical uncertainty (SU) value for calculation, reducing the redundancy of data. In terms of feature selection, the TOPSIS method is used instead of the traditional filtering method, which is applied to analysis and decision making, and important features are selected from seven filtering methods. Finally, in order to validate the classification ability of the proposed model, k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural networks (ANN) are used as independent classifiers. The effectiveness of the proposed model is evaluated by applying two bearing datasets, namely the current dataset of motor vibration signals and the dataset of bearing motors provided by Case Western Reserve University (CWRU). The results show that the comparison of the proposed model with other models shows the feasibility of this study. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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14 pages, 6390 KiB

Open AccessArticle

Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System

by Izaz Raouf, Prashant Kumar, Hyewon Lee and Heung Soo Kim

Mathematics 2023, 11(4), 945; https://doi.org/10.3390/math11040945 - 13 Feb 2023

Cited by 11 | Viewed by 2286

Abstract

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In smart factories, robotic components are vulnerable to failure due to various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection and diagnosis (FDD) is important to keep the industrial operation smooth. Previously, only the unloaded-based FDD algorithms were considered for the industrial robotic system. In the industrial environment, the robot is working under various working conditions such as speeds, loads, and motions. Hence, to reduce the domain discrepancy between the lab scale and the real working environment, we conducted experimentations under various working conditions. For that purpose, an extensive experimental setup is prepared to perform a series of various experiments mimicking the real environmental condition. In addition, in previous research work, various machine learning (ML) and deep learning (DL) approaches were proposed for robotic arm component fault detection. However, various issues are related to the DL and ML approaches. The ML models are problem-specific, and complex in computations. The DL model needs a huge amount of data. The DL model is composed of various layers that have not been thoroughly explored; as a result, the fault detection model lacks a comprehensive explanation. To overcome these issues, the transfer learning (TL) model is considered with the diverse experimental scenarios. The main contribution is to increase the generalization capabilities of the robotic PHM in the context of previously available research work. For that purpose, the VGG16 model is used because of its autonomous feature extractions for fault classification. The data are collected under a variety of different operating conditions such as loadings, speeds, and motion patterns. The 1D signal is converted to a 2D signal (scalogram) to perform the TL model. The proposed approach shows effective fault detection performance and has the capabilities of generalization under variable working conditions. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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17 pages, 9198 KiB

Open AccessArticle

Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions

by Hyewon Lee, Izaz Raouf, Jinwoo Song, Heung Soo Kim and Soobum Lee

Mathematics 2023, 11(2), 398; https://doi.org/10.3390/math11020398 - 12 Jan 2023

Cited by 13 | Viewed by 2734

Abstract

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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14 pages, 3912 KiB

Open AccessArticle

Deep Transfer Learning Framework for Bearing Fault Detection in Motors

by Prashant Kumar, Prince Kumar, Ananda Shankar Hati and Heung Soo Kim

Mathematics 2022, 10(24), 4683; https://doi.org/10.3390/math10244683 - 9 Dec 2022

Cited by 17 | Viewed by 2547

Abstract

The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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Review

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37 pages, 6691 KiB

Open AccessFeature PaperReview

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

by Prashant Kumar, Salman Khalid and Heung Soo Kim

Mathematics 2023, 11(13), 3008; https://doi.org/10.3390/math11133008 - 6 Jul 2023

Cited by 12 | Viewed by 4679

Abstract

The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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28 pages, 4240 KiB

Open AccessReview

Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques

by Salman Khalid, Jinwoo Song, Izaz Raouf and Heung Soo Kim

Mathematics 2023, 11(8), 1767; https://doi.org/10.3390/math11081767 - 7 Apr 2023

Cited by 13 | Viewed by 4553

Abstract

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development. Full article

(This article belongs to the Special Issue Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis)

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