Intelligent Fault Diagnosis System Research Articles

Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering.

Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System

In view of the great pressure of operation and maintenance of power energy data collection terminal after putting into operation, an intelligent fault diagnosis system for power equipment terminal based on computer was developed. This paper proposes an Agent based on autonomy, promotility, cooperation and interactivity. It also provides a flexible interactive communication interface and load balancing optimization method. It can meet a variety of data transmission specifications and a large number of data service transmission requirements. The SA-KNN algorithm is used to analyze the power quality. Methods The operation data of maintenance electricity were collected. Learning and cluster analysis are carried out by using relevant data, and the calculation model of automatic check device failure is established. At the same time, the outlier points in the collected data are used to accurately locate the fault points of the power grid. This method can realize the comprehensive fault diagnosis of the power grid by using the interaction between the power grid and each component.

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network, is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB’s life cycle, and facilitate the application of intelligent fault diagnosis system.

Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN)

As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods.

A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features

Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal features extracted from healthy, chipped, and broken tooth gear health categories. The performance of the CNN is compared with conventional machine learning models, including Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) classifiers. Experimental investigations highlight CNN’s remarkable performance. With vibration features, the CNN achieved 96.78% accuracy, surpassing SVM (84.89%), NB (81.56%), and RF (85.11%). The CNN attained 100% accuracy when utilizing thermal features, while SVM, NB, and RF achieved 91.11%, 88.89%, and 96.51% accuracies, respectively.

An intelligent online fault diagnosis system for gas turbine sensors based on unsupervised learning method LOF and KELM

The performance of gas turbine inevitably grades slowly in service. In order to obtain high-precision state assessment, an intelligent online real-time sensor fault diagnosis algorithm was proposed in this paper. This method can automatically establish an accurate diagnosis model in a short time, realize the rapid diagnosis of sensor faults, and can effectively solve the problem of operation data imbalance. Firstly, wavelet analysis quickly converts the high-dimensional time-series sensor signal into a low-dimensional feature vector. Then, the unsupervised method LOF was used to screen the abnormal sensor signals. Finally, the KELM is used to achieve fast online identify the fault modes of the sensors. This fault diagnosis system achieves a faster diagnosis speed while ensuring the accuracy. The effectiveness of the method proposed in this paper was verified on the operational data of a gas turbine, the diagnosis time is about 0.233 s, which greatly increases the diagnosis speed compared with other methods, at the same time, the proposed method can guarantee more than 95 % of diagnosis accuracy.

Research on CNN-LSTM DC power system fault diagnosis and differential protection strategy based on reinforcement learning

Introduction: With the development of artificial intelligence technology, more and more fields are applying deep learning and reinforcement learning techniques to solve practical problems. In the power system, both the direct current (DC) power system and the power grid substation are important components, and their reliability and stability are crucial for production efficiency and safety. The power grid substation is used to convert power from high-voltage transmission lines to low-voltage transmission lines, or from alternating current to direct current (or vice versa), in order to efficiently transmit and distribute power in the power system. However, diagnosing faults and designing cascaded protection strategies has always been a challenge due to the complexityand uncertainty of the DC power system.Methods: To improve the reliability and stability of the DC power system and power grid substation, this paper aims to develop an intelligent fault diagnosis system and cascaded protection strategy to reduce faults and downtime, lower maintenance costs, and increase production efficiency. We propose a method based on reinforcement learning and a convolutional neural network-long short-term memory (CNN-LSTM) model for fault diagnosis and cascaded protection strategy design in the DC power system. CNN is used to extract features from raw data, while LSTM models time-series data. In addition, we use reinforcement learning to design cascaded protection strategies to protect the power system from the impact of faults.Results: We tested our method using real 220V DC power system data in experiments. The results show that our method can effectively diagnose faults in the DC power system and formulate effective cascaded protection strategies.Discussion: Compared with traditional methods, this intelligent method can diagnose faults faster and more accurately, and formulate better cascaded protection strategies. This method helps reduce maintenance costs, increase production efficiency, and can be applied to other fields.

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

AbstractThe Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness.

Dynamic Offloading Loading Optimization in Distributed Fault Diagnosis System with Deep Reinforcement Learning Approach

Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online.

Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan

Abstract Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudo-colour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.

Research on Vibration Fault Diagnosis of Steam Turbine Based on Equipment Tree

As the main major equipment in the power plant, the steam turbine has a complex structure and poor operating environment. Its reliability is very important for the power plant. Intelligent fault diagnosis of steam turbines is an important part of the construction of smart power plants. As a complex system, the operation state of a steam turbine may be affected by a variety of faults. Different faults are interconnected and excited each other, which is often caused by the concurrence of other faults due to the occurrence of one fault. The external phenomenon produced is the result of the comprehensive action of many factors. The intelligent fault diagnosis system of steam turbines is based on traditional parameter monitoring and further analyzes on the basis of monitoring to find out the cause of the fault and gives maintenance suggestions. It includes diagnosing the faults that have occurred to prevent further deterioration and predicting the faults that have not occurred to prevent the occurrence of faults so as to achieve the purpose of fault diagnosis, pre-diagnosis, and intelligent maintenance.

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

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.

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digitised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportunity to develop technical language supervision (TLS) solutions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on Sentence-BERT and contrastive learning based zero-shot inference on annotated industry data.

Bearing fault diagnosis using signal processing and machine learning techniques: A review

In the majority of machines, bearings are among the most crucial components. Bearings are so important that they have been the subject of intensive research and ongoing development throughout the years. Often, bearing fails to reach its expected service life, resulting in failures that cause economic losses. Therefore, there has been a growing interest in research on bearing failure diagnosis systems due to the availability of condition monitoring techniques. Fault feature extraction techniques with the application of signal processing methods and machine learning techniques introduce an Intelligent Fault Diagnosis system that can identify and diagnose the bearing faults. Many researchers have been interested in such techniques in recent decades, which use artificial intelligence to diagnose machine health conditions. In this article, the authors have explored certain fault diagnosis methodologies based on signal processing and machine learning. From the discussed literature review, a research gap for future work has been defined.

RADIS: A real-time anomaly detection intelligent system for fault diagnosis of marine machinery

By enhancing data accessibility, the implementation of data-driven models has been made possible to empower strategies in relation to O&M activities. Such models have been extensively applied to perform anomaly detection tasks, with the express purpose of detecting data patterns that deviate significantly from normal operational behaviour. Due to its preeminent importance in the maritime industry to adequately identify the behaviour of marine systems, the Real-time Anomaly Detection Intelligent System (RADIS) framework, constituted by a Long Short-Term Memory-based Variational Autoencoder in tandem with multi-level Otsu’s thresholding, is proposed. RADIS aims to address the current gaps identified within the maritime industry in relation to data-driven model applications for enabling smart maintenance. To assess the performance of such a framework, a case study on a total of 14 parameters obtained from sensors installed on a diesel generator of a tanker ship is introduced to highlight the implementation of RADIS. Results demonstrated the capability of RADIS to be part of a diagnostic analytics tool that will promote the implementation of smart maintenance within the maritime industry, as RADIS detected an average of 92.5% of anomalous instances in the presented case study.

An Intelligent Process Fault Diagnosis System based on Andrews Plot and Convolutional Neural Network

This paper proposes an intelligent process fault diagnosis system based on the techniques of Andrews plot and convolutional neural network. The proposed fault diagnosis method extracts features from the on-line process measurements using Andrews function. To address the uncertainty of setting the proper dimension of extracted features in Andrews function, a convolutional neural network is used to further extract diagnostic information from the Andrews function outputs. The outputs of the convolutional neural network are then fed to a single hidden layer neural network to obtain the final fault diagnosis result. The proposed fault diagnosis system is compared with a conventional neural network based fault diagnosis system. Applications to a simulated CSTR process show that the proposed fault diagnosis system gives much better performance than the conventional neural network based fault diagnosis system. It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.

Intelligent Fault Diagnosis Tool For Power Line Systems

The paper focuses on the intelligent fault diagnosis system for automatic analysis of a huge amount of images with 4K resolution collected during flight inspection of the overhead power lines with the use of aerial platforms. The developed system can be used to detect and locate anomalies of power line structures e.g. anomalies of towers, conductors, dampers, insulators, etc. The purpose of the paper is to present the general architecture of the system and the results obtained during inspection of thousands of kilometer flights of power lines. The proposed system is based on the predefined deep neural network called Faster R-CNN which is dedicated for solving real-time object detection problems. The faster R-CNN-based neural model created on COCO dataset was additionally retrained by the authors applying images gathered as a result of flight inspections in order to obtain the high performance of the whole system. The comprehensive verification tests were carried out to prove the merits of the proposed solution.

An Intelligent Fault Analysis and Diagnosis System for Electromagnet Manufacturing Process Based on Fuzzy Fault Tree and Evidence Theory

Because an electromagnet has a complex structure and manufacturing process, it is difficult to analyze the overall failure of the electromagnet. In order to solve this problem, a fault intelligent analysis and diagnosis system based on fuzzy fault tree and evidence theory is proposed in this paper. First, the failure structure and fuzzy fault tree are generated according to the experience. Second, the probability of failure caused by basic events is obtained based on the data statistics of the insufficient holding force of the electromagnet in the past. Then, the probability of the basic events is given by using the synthesis rules of evidence theory. Next, the belief interval of the basic event is set as the fuzzy number, and the intelligent analysis is completed by using the calculated fuzzy importance. Finally, the validity and feasibility of the proposed method is proved by using the failure of insufficient retention force in the electromagnet manufacturing process as an example.

A Two-Step Unsupervised Learning Approach to Diagnose Machine Fault Using Big Data

The modern industrial sector requires an intelligent fault diagnosis system to ensure reliable and safe processingsince traditional methods require expert diagnosis, which consumes time and requires labor. Furthermore, diagnosticresults are influenced by the expert’s expertise and in-depth knowledge of the machine. The objective of this paper isto solve the manual intervention problem and improve the fault diagnosis. We propose a novel two-stage unsupervisedlearning algorithm based on artificial intelligence (AI) that learns fault features efficiently from raw vibration signals.To accomplish the aforementioned goal, we encapsulate the two-stage learning technique such as sparse filtering andRectified Linear Unit (ReLU) regression function. As a first step, we used a two-layer neural network sparse filteringprocedure to extract vibration signals’ features. Based on vibration signals, ReLU regression determines the healthcondition of the machine in the second phase. ReLU is a linear function that improves the performance of neural network training. Here we utilized a sigmoid and softmax regression function to compare the performance of ReLU. Thesigmoid function works well for binary classification, whereas softmax works well for multiclass classification. A database of motor-bearing vibration signals containing signals about four different health conditions of machines, suchas Inner race faults (IF), Outer race faults (OF), and rolling faults (RF). The sparse filter is evaluated on different inputand output dimensions, which significantly increases the learning accuracy. We classified the health condition usingReLU and achieved 93.8% accuracy, which is higher than sigmoid and softmax. Through the two-step learning process, machine fault diagnosis is enhanced, as well as big data is effectively handled.