Fault Diagnosis Technology Research Articles

Fault diagnosis of real-scenario battery systems based on modified entropy algorithms in electric vehicles

Entropy algorithm is an efficient fault diagnosis technology gradually gaining popularity, which is highly promising in terms of battery safety protection in electric vehicles. This paper compares two entropy algorithms commonly used for battery fault diagnosis and their ability to diagnose abnormal fluctuations implied in the pre-fault phase. By exploring the influence of key calculation factors in the entropy algorithm on the fault diagnosis effect, this study pioneered the discovery of a significant normal distribution pattern and the logarithmic relationship between the diagnostic effect and the size of the Shannon entropy's calculation window and multi-scale sample entropy's scale factor, respectively. The relationship between computation time and computation factor was found to provide a theoretical basis for improving diagnosis efficiency through the rational selection of computation factors. Furthermore, based on actual vehicle fault data verification, a multi-level diagnosis strategy with strong robustness and generality is proposed using statistical methods. More importantly, this study continues to explore the importance of deeper mining of different entropy features, and the proposed control strategies are of high practical significance for battery fault diagnosis/prognosis in real-world vehicle applications.

Research on Substation Monitoring and Fault Diagnosis Based on Distributed Computing and Artificial Neural Network

In the Energy Conversion for Next-Generation Smart Cities, intelligent substation plays an important role in the power conversion. As an important guarantee for the stable operation of intelligent substation, the research on fault diagnosis technology is particularly important. In this paper, the acoustic characteristic diagnosis of substation equipment (take transformers for example) is researched and the application of “Voice Recognition + artificial neural network (ANN)” technology in substation fault diagnosis is analyzed. At the same time, the continuous online monitoring of the intelligent substation equipment will produce a large amount of monitoring data, which needs to be analyzed timely and effectively to understand the operating status of the equipment accurately. Because of this, this paper adopts distributed computing by establishing a real-time distributed computing platform, using open source technology to store the online monitoring of sound data into the computing platform for data processing to achieve the purpose of automatic fault detection and analysis. The results show that distributed computing can realize the intelligent analysis, storage, and visualization of equipment data in the substation, which provides data support for fault diagnosis. Besides, the fitting accuracy rates of ANN model are 95.123% for training process and the fitting accuracy rates of ANN model are 99.353% for training process and the overall fitting accuracy rates of ANN model are 95.478% and the error between the predicted value and the actual value of the 5 sound signals is within 5% in the fault diagnosis process. Consequently, the ANN model can accurately identify each fault sound of substation and achieve the purpose of fault diagnosis.

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

Fault Diagnosis for Multilevel Converters Based on an Affine-Invariant Riemannian Metric Autoencoder

Multilevel converters play an important role in power electronic systems. In recent years, data-driven fault diagnosis technologies for multilevel converters have rapidly developed and are generally based on feature engineering and intelligent classification algorithms. However, the fault detection rate, speed, and stability still need to be improved, especially for complex converter systems. In this article, based on a probabilistic autoencoder architecture, an affine-invariant Riemannian metric autoencoder (AIRMAE) is proposed for feature extraction, and combined with classifiers, a supervised learning fault diagnosis method for multilevel converters is constructed. The proposed AIRMAE is able to investigate low-dimensional representations from high-dimensional data manifolds while preserving sufficient valid information. Based on a five-level nested neutral-point piloted converter, the effectiveness of the proposed method is verified by experiments. The performance is superior to that of existing state-of-the-art fault diagnosis methods, with high detection accuracy, good stability, and strong waveform reconstruction ability.

Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.

Analysis of online diagnosis and repair technology of lead acid battery faults

Lead-acid battery, as a basic component of DC power supply equipment in power communication system, is also known as maintenance-free battery. It is believed that it does not need maintenance treatment in the initial stage of application research. However, as an electrochemical product, it has relatively high requirements for both use environment and operation. It is the main problem discussed in electric power industry at present. In this paper, on the basis of understanding the research status of online fault diagnosis technology of lead-acid battery, according to the basic principle of lead-acid battery and online detection system, discharge treatment of two groups of tested single battery load, calculate and analyze the battery resistance value. The final experimental results show that this method can solve the safety hazard of the whole battery line discharge, control the equipment cost, and ensure the fault location is more accurate and effective.

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

Research on fault diagnosis method of turbocharger rotor based on Hu-SVM-RFE

Abstract Several parameters need to be monitored for turbocharger rotor faults and the overlap between different fault parameters as well as the redundancy of data, which leads to increased calculation time and reduced classification accuracy. To improve the recognition rate of turbocharger rotor faults, a recursive elimination method based on the support vector machine-recursive feature elimination (SVM-RFE) combined with improved Hu invariant moments is developed for the axial orbit feature extraction of turbocharger rotor with rotor fault. Firstly, improved Hu-invariant moments are extracted for different rotor fault axis orbits, and then the feature ranking and selection are performed by the SVM-RFE method to filter out the feature combinations with higher classification recognition rates. Then, the feature matrix of the Hu-SVM-RFE algorithm screening combination was identified for classification using each of the three diagnostic algorithms. The results show that the optimal feature subset obtained by the Hu-SVM-RFE method can ensure the richness of the fault information of the turbocharger rotor with small number of features. And, a high classification rate can be obtained with low time consumption in combination with the probabilistic neural network (PNN) algorithm. Therefore, Hu-SVM-RFE feature screening method combined with PNN fault diagnosis technology has high accuracy and efficiency, which is of great significance for online fault identification of the supercharger rotor.

A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines

With the rapid development and increasing energy production capacity of high-power wind turbines, a corresponding increase in maintenance requirements has been observed. Reducing the failure rate of wind turbines is a critical objective, alongside decreasing affiliated operation and maintenance costs. This review focuses on the status monitoring, fault diagnosis, fault prediction, and status evaluation of wind turbines. The early fault diagnosis of wind turbines is explored with regard to existing condition monitoring technology. Moreover, the current mathematics-based fault diagnosis and smart fault diagnosis technologies are further explored. Through comprehensive investigation, this paper summarizes the research status of wind turbine fault prediction and complete machine status evaluation, conclusively presenting relevant research points and trends in the fault diagnosis, fault prediction, and status assessment of high-power wind turbines.

Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults.

Towards High-Safety Lithium-Ion Battery Diagnosis Methods

With the great development of new energy vehicles and power batteries, lithium-ion batteries have become predominant due to their advantages. For the battery to run safely, stably, and with high efficiency, the precise and reliable prognosis and diagnosis of possible or already occurred faults is a key factor. Based on lithium-ion batteries’ aging mechanism and fault causes, this paper summarizes the general methods of fault diagnosis at a macro level. Moreover, lithium-ion battery fault diagnosis methods are classified according to the existing research. Therefore, various fault diagnosis methods based on statistical analysis, models, signal processing, knowledge and data-driven are discussed in depth. Finally, the main challenges faced by fault diagnosis technology and future directions for possible research and development are put forward.

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy.

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings.

Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection

Onshore wind turbines are primarily installed in high-altitude areas with good wind energy resources. However, in winter, the blades are easy to ice, which will seriously impact their aerodynamic performance, as well as the power and service life of the wind turbine. Therefore, it is of great practical significance to predict wind turbine blade icing in advance and take measures to eliminate the adverse effects of icing. Along these lines, three approaches to supervisory control and data acquisition (SCADA) data feature selection were summarized in this work. The problems of imbalance between positive and negative sample datasets, the underutilization of SCADA data time series information, the scarcity of high-quality labeled data, and weak model generalization capabilities faced by data-driven approaches in wind turbine blade icing detection, were reviewed. Finally, some future trends in data-driven approaches were discussed. Our work provides guidance for the use of technical means in the actual detection of wind turbine blades. In addition, it also gives some insights to the further research of fault diagnosis technology.

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

AbstractCurrently, accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited. A novel multi‐scale CNN‐LSTM neural network (MSCNN‐LSTMNet) is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis. First, a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised. Considering the essential characteristics of measured vibration signals at different scales, a multi‐scale convolutional NN (CNN) block is designed to realize multi‐scale feature extraction. Specifically, multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features, enhancing the robustness of the model. On this basis, the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisation performances. The effectiveness of MSCNN‐LSTMNet is validated by experimental results of both one‐ and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions. The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods. Under strong noise conditions (−10 dB signal‐to‐noise ratio) for four datasets, MSCNN‐LSTMNet obtained 97.561% average accuracy, while average accuracy for random forest, deep neural network, CNN and MSCNNNet were 73.828%, 79.544%, 82.247%, and 89.741%, respectively. Moreover, for 11 noise generalisation tasks between different noise levels, MSCNN‐LSTMNet obtained at least 96.679%, 97.849%, 98.892%, and 94.010% accuracy on the four datasets, which are much higher than those of the existing methods.

A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information

In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. Experiments evaluate the effectiveness of the proposed method.

A new test method for simulating wear failure of hydraulic pump slipper pair under high-speed and high-pressure conditions

In practical engineering, it is very difficult to obtain data on the slipper wear of hydraulic pumps, especially under high-speed, high-pressure conditions, which limits the development of fault diagnosis technology for hydraulic pumps. At present, a test method that can accurately simulate the operating state of the slipper pair under high-speed and high-pressure conditions does not exist. The reliable load-bearing design of the slipper pair is difficult to carry out effectivetest verification, which limits the development of high-speed and high-pressure piston pumps. Therefore, an experimental design method was proposed to directly simulate the high-speed, high-pressure friction state of the slipper pair based on the change law of reprinting residual pressing force.

Active detection fault diagnosis and fault location technology for LVDC distribution networks

Under the rapid development of direct current (DC) transmission technology, the interconnection between converter stations in the DC network has been further improved, and a looped network topology has been gradually formed. Practical applications have shown that loop troubleshooting takes a long time to perform and requires substantial field experience, which has considerably reduced the efficiency of troubleshooting and the safety factor of low-voltage direct current (LVDC) distribution networks. The complex, transient characteristics and short duration of power electronic system faults limit the application of passive detection and protection technology, raising difficulties in DC line fault detection and location. To adapt to the development of power systems, this paper presents an active detection fault diagnosis and fault location technology for LVDC distribution networks based on a converter. After a line fault occurs, the fault type is identified by injecting detection signals into the fault line through the converter, and the fault location is determined by calculating harmonic impedance. The method can detect the fault of the system in time to reduce loss when the system on the converter side is faulty but can still run. It effectively utilizes the converter equipment in power systems and does not require the installation of additional detection devices, which is largely unaffected by fault resistance in fault location, and has accurate detection results and fast speed.

Retracted: Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

Retracted: Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

Fault States Diagnosis of Marine Diesel Engine Valve Based on a Modified VGG16 Transfer Learning Method

The marine diesel engine is an important power machine for ships. Traditional machine learning methods for diesel engine fault diagnosis usually require a large amount of labeled training data, and the diagnosis performance may decline when encounters vibrational and environmental interference. A transfer learning convolutional neural network model based on VGG16 is introduced for diesel engine valve leakage fault diagnosis. The acquired diesel engine cylinder head vibration signal is first converted to time domain, frequency domain, and wavelet decomposition images. Secondly, the VGG16 deep convolutional neural network is pretrained using the ImageNet dataset. Subsequently, fine tuning the network based on the pretrained basic parameters and image enhancement methods. Finally, the well‐trained model is adopted to train and test the target dataset. In addition, the cosine annealing learning rate setting method is used to make the learning rate close to the global optimal solution. Experimental results show that the proposed method has higher accuracy and better robustness against noise with a small sample dataset than traditional methods and deep learning models. This study not only demonstrates a novel view for the diagnosis of marine diesel engine valve leakage, but also provides an applicable diagnosis method for other similar issues.