Novel Method For Fault Diagnosis Research Articles

Cross-Attention Transformer-Based Domain Adaptation: A Novel Method for Fault Diagnosis of Rotating Machinery With High Generalizability and Alignment Capability

Cross-Attention Transformer-Based Domain Adaptation: A Novel Method for Fault Diagnosis of Rotating Machinery With High Generalizability and Alignment Capability

A novel method for fault diagnosis of fluid end of drilling pump under complex working conditions

Accurate fault diagnosis of drilling pump under complex operating conditions poses a significant challenge in drilling operations. This paper addresses the difficulty of extracting fault features from the timing signals of the drilling pump's fluid end under complex working conditions by proposing a method for generating images to represent multidimensional timing signals. Additionally, a multiscale deep recursive inverse residual neural network (MDRIRNN) is introduced to achieve fault diagnosis of the fluid end under such conditions. Firstly, a multidimensional relative position matrix (MRPM) is proposed, which transforms one-dimensional timing signals into three-dimensional images using a 3D data structure. This approach effectively distinguishes the features of different timing signals by adding dimensions. Next, a recursive inverse residual block is designed, and multiscale learning is incorporated to form the MDRIRNN. This model enables the extraction of feature information from the multidimensional images at different scales, thereby enhancing the fault diagnosis process. Subsequently, a fault diagnosis experiment is conducted, demonstrating an average diagnostic accuracy of 96.14 %. Furthermore, the adaptability of the proposed method is validated using the MFPT dataset, achieving a diagnostic accuracy of 99.88 %. This novel method provides a prospecting approach for equipment fault diagnosis under complex operating conditions.

A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis

Cell voltage inconsistency in a battery pack is an important signal released by the deterioration of battery performance. In this paper, voltage inconsistency is categorized into static inconsistency and dynamic inconsistency, and the latter contains progressive fluctuation fault and sudden fluctuation fault. For voltage dynamic inconsistency, this paper innovatively proposes a fault diagnosis and type identification method based on weighted Euclidean distance evaluation and statistical analysis. Specifically, firstly, the Euclidean distance evaluation method is used to quantify the cumulative length of the voltage curve of each individual cell in the time window, and at the same time, different forgetting factors are weighted on the voltage line segments to mitigate the fault diagnostic delay and memory effect defects of the time window. Then, a voltage abnormality evaluation coefficient is introduced to characterize the degree of inconsistent fluctuation of the cell voltage, and statistical methods are used to find a reasonable threshold. Further, fault type identification algorithm layer is conducted to identify the type of cell voltage fluctuation by using the optimized correlation coefficient method while the cell is detected by the fault diagnosis algorithm layer. Finally, the effectiveness of the proposed fault diagnosis strategy is verified by experimental data, and an online platform is utilized to obtain voltage data with different fault characteristics to test the practical application of the method. By comparing the proposed method with various data-driven methods with the same data evaluation significance, the results show that the method in this paper is more robust to static voltage inconsistency and the length of the time window, and is capable to recognize the various data patterns of potential threats, with higher accuracy and computational efficiency.

A Hybrid Approach Combining Data-Driven and Signal-Processing-Based Methods for Fault Diagnosis of a Hydraulic Rock Drill

This study presents a novel method for fault diagnosis of a hydrostatic rock drill. Hydraulic rock drills suffer from both domain discrepancy issues that arise due to their harsh working environment and indivisible difference. As a result, fault diagnosis is very challenging. To overcome these problems, we propose a novel diagnosis method that combines both data-driven and signal-process-based methods. In the proposed approach, data-driven methods are employed for overall fault classification, using domain adaptation, metric learning, and pseudo-label-based deep learning methods. Next, a signal-process-based method is used to diagnose the specific fault by generating a reference signal. Using the combined approach, the fault-diagnosis performance was 100%; the proposed method was able to perform well even in cases with domain discrepancy.

A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function Jψij—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems.

Fault diagnosis of automobile power seat with acoustic analysis and retrained SVM based on smartphone

Automobile power seats (APS) fault diagnosis method research is crucial for manufacturers to adjust production strategies and improve economic efficiency. Although acoustic analysis has been used in production process to detect APS faults, current methods are inefficient and cannot deal with sharp noise interference. Thus, this paper presents a novel method for fault diagnosis of APS based on smartphone. Firstly, we propose an Adaptive Filter Banks Method (AFBM) to extract spectral features and reduce the subsequent computation, which solves the fixed spectrum resolution problem of short-time Fourier transform. Secondly, we use an outlier detection algorithm based on K-Nearest Neighbor and statistical theory to exclude noise-interfered samples. Finally, we propose a retrained support vector machine (SVM) model to identify the fault according to the clustering combination of different faults. To verify the effectiveness of the proposed method, we measured the actual acoustic signal of a typical APS 20DL80X9D-7. We use smartphone instead of professional microphone and processor to adapt to different application scenes. The accuracy and immediacy of fault identification results verify the superiority of our method.

A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks

The data-driven intelligent fault diagnosis method of rolling bearings has strict requirements regarding the number and balance of fault samples. However, in practical engineering application scenarios, mechanical equipment is usually in a normal state, and small and imbalanced (S & I) fault samples are common, which seriously reduces the accuracy and stability of the fault diagnosis model. To solve this problem, an auxiliary classifier generative adversarial network with spectral normalization (ACGAN-SN) is proposed in this paper. First, a generation module based on a deconvolution layer is built to generate false data from Gaussian noise. Second, to enhance the training stability of the model, the data label information is used to make label constraints on the generated fake data under the basic GAN framework. Spectral normalization constraints are imposed on the output of each layer of the neural network of the discriminator to realize the Lipschitz continuity condition so as to avoid vanishing or exploding gradients. Finally, based on the generated data and the original S & I dataset, seven kinds of bearing fault datasets are made, and the prediction results of the Bi-directional Long Short-Term Memory (BiLSTM) model is verified. The results show that the data generated by ACGAN-SN can significantly promote the performance of the fault diagnosis model under the S & I fault samples.

A Novel Method for Fault Diagnosis of Rotating Machinery.

When rotating machinery fails, the consequent vibration signal contains rich fault feature information. However, the vibration signal bears the characteristics of nonlinearity and nonstationarity, and is easily disturbed by noise, thus it may be difficult to accurately extract hidden fault features. To extract effective fault features from the collected vibration signals and improve the diagnostic accuracy of weak faults, a novel method for fault diagnosis of rotating machinery is proposed. The new method is based on Fast Iterative Filtering (FIF) and Parameter Adaptive Refined Composite Multiscale Fluctuation-based Dispersion Entropy (PARCMFDE). Firstly, the collected original vibration signal is decomposed by FIF to obtain a series of intrinsic mode functions (IMFs), and the IMFs with a large correlation coefficient are selected for reconstruction. Then, a PARCMFDE is proposed for fault feature extraction, where its embedding dimension and class number are determined by Genetic Algorithm (GA). Finally, the extracted fault features are input into Fuzzy C-Means (FCM) to classify different states of rotating machinery. The experimental results show that the proposed method can accurately extract weak fault features and realize reliable fault diagnosis of rotating machinery.

A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks

Cyber-attacks can tamper with measurement data from physical systems via communication networks of smart grids, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section. Accordingly, a fault diagnosis method should first determine whether a fault is actually present; however, current diagnosis methods of power systems struggle to achieve this goal. This paper proposes a novel method for fault diagnosis based on memory spiking neural P systems, which can distinguish false faults caused by measurement tampering attacks. The proposed method consists of three modules with the functions of suspicious fault section detection, measurement tamper attack identification and fault diagnosis, respectively. The suspicious fault section detection module is used to find candidate sections to reduce the fault diagnosis scope. The attack identification module is designed to identify whether a possibly faulty section is under the measurement tampering attack or not. The fault diagnosis module is devised to diagnose true faults, detecting both the fault sections and their corresponding fault types. To achieve the above goals, inspired by the memory recall mechanism of human brains, a memory spiking neural P system and a corresponding general matrix reasoning algorithm are proposed, which can synthetically utilize the remote measurements and remote signals via a new modeling mechanism. Finally, case studies based on the IEEE 14 and IEEE 118 bus systems verify the feasibility and effectiveness of the proposed method.

A novel method for fault diagnosis in rolling bearings based on bispectrum signals and combined feature extraction algorithms

A novel method for fault diagnosis in rolling bearings based on bispectrum signals and combined feature extraction algorithms

A Novel Method for Fault Diagnosis of the Two-Input Two-Output Nonlinear Mass-Spring-Damper System Based on NOFRF and MBPCA

For fault diagnosis of the two-input two-output mass-spring-damper system, a novel method based on the nonlinear output frequency response function (NOFRF) and multiblock principal component analysis (MBPCA) is proposed. The NOFRF is the extension of the frequency response function of the linear system to the nonlinear system, which can reflect the inherent characteristics of the nonlinear system. Therefore, the NOFRF is used to obtain the original fault feature data. In order to reduce the amount of feature data, a multiblock principal component analysis method is used for fault feature extraction. The least squares support vector machine (LSSVM) is used to construct a multifault classifier. A simplified LSSVM model is adopted to improve the training speed, and the conjugate gradient algorithm is used to reduce the required storage of LSSVM training. A fault diagnosis simulation experiment of a two-input two-output mass-spring-damper system is carried out. The results show that the proposed method has good diagnosis performance, and the training speed of the simplified LSSVM model is significantly higher than the traditional LSSVM.

Ball bearing fault detection via multi-sensor data fusion with accelerometer and microphone

Early detection of defects in bearings is essential to avoid the complete failure of machinery and the associated costs. This study presents a novel method for fault diagnosis of bearings using sensor fusion with a microphone and an accelerometer. The system has five modules, namely data acquisition, signal processing, feature extraction, classification and decision-making. A test-rig is designed to collect acoustic and vibration signals. Then, for each signal, indices are calculated in the time and frequency domains. After using principal component analysis (PCA) for feature extraction, the k-nearest neighbours (kNN) method is used in the classification module. Finally, a decision on the kind of fault and its size is made based on the decision fusion module. The aim of this study is to propose a fusion method to improve the effectiveness and reliability of bearing defect diagnosis compared to what can be achieved with vibration or acoustic measurements alone. The results obtained from this preliminary study show that condition monitoring using the accelerometer is the more effective technique for determining the type of fault, while the microphone is effective for classifying the size of fault. Experimental results also confirm these findings.

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning.

In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods.

Parametric Fault Diagnosis Based on Fuzzy Cerebellar Model Neural Networks

This paper aims to propose a more efficient classifier and applies it to parametric fault diagnosis for converter circuit. A novel method for fault diagnosis based on a fuzzy cerebellar model neural network (FCMNN) in a dual-buck bidirectional dc–ac converter circuit is proposed. The proposed method uses fast Fourier transform to analyze the fault signal and effectively extract fault characteristics. The parameter adaptation laws of the classifier are derived to achieve fast and effective training and testing efficiency. After training, the neural network can identify the working state of capacitor and inductor, and achieve parametric fault diagnosis. The training samples and test samples are collected through a dual-buck bidirectional dc–ac converter circuit simulation system and a practical experimental platform. A back-propagation neural network, support vector machine, and the proposed FCMNN are performed for circuit fault diagnosis, and the results of these three methods are compared. Both the simulation results and experimental results show that the proposed FCMNN can effectively diagnose parametric faults, such as the component degradation of the capacitor and inductor, with fast learning and diagnosis speed, higher diagnostic accuracy, and good antinoise ability.

Adaptive Online Fault Diagnosis in Autonomous Robot Swarms.

Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviors. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however we propose that diagnosis is a feature of active fault tolerance that is necessary if swarms are to obtain long-term autonomy. This paper presents a novel method for fault diagnosis that attempts to imitate some of the observed functions of natural immune system. The results of our simulated experiments show that our system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.

Fault Diagnosis of Gearboxes Using Nonlinearity and Determinism by Generalized Hurst Exponents of Shuffle and Surrogate Data.

Vibrations of defective gearboxes show great complexities. Therefore, dynamics and noise levels of vibrations of gearboxes vary with operation of gearboxes. As a result, nonlinearity and determinism of data can serve to describe running conditions of gearboxes. However, measuring of nonlinearity and determinism of data is challenging. This paper defines a two-dimensional measure for simultaneously quantifying nonlinearity and determinism of data by comparing generalized Hurst exponents of original, shuffle and surrogate data. Afterwards, this paper proposes a novel method for fault diagnosis of gearboxes using the two-dimensional measure. Robustness of the proposed method was validated numerically by analyzing simulative signals with different noise levels. Moreover, the performance of the proposed method was benchmarked against Approximate Entropy, Sample Entropy, Permutation Entropy and Delay Vector Variance by conducting two independent gearbox experiments. The results show that the proposed method achieves superiority over the others in fault diagnosis of gearboxes.

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu’s norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

Intelligent Fault Diagnosis in a Power Distribution Network

This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT), the improved multiscale permutation entropy (IMPE), and the linear local tangent space alignment (LLTSA) with the extreme learning machine (ELM), is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.