Machine Fault Diagnosis Research Articles

A noise-enhanced machine fault diagnosis method with multi-channel information fusion

ABSTRACT Condition health monitoring and fault diagnosis is able to safeguard against potential threats to machines. Nevertheless, the acquired signals by using a plenty of sensors are often accompanied by noise and meanwhile the useful information contained in the signals is not both complete and reliable, thereby together reducing the accuracy and stability of the fault diagnosis. Therefore, this paper proposes a noise-enhanced intelligent fault diagnosis method with multi-channel information fusion. Here, it would inject the varying levels of Gaussian noise into multi-channel input data as well as intelligent diagnostic models, with a view to elucidating the benefits of noise. Furthermore, the proposed method is validated through three experiments. The experimental results demonstrate that the diagnostic accuracy of the hydraulic motor and the two different bearings reaches 99.85%, 99.89%, and 99.85% by using the proposed method, respectively, which are increased by 2.50%, 4.09%, and 2.50%, respectively, compared with one without the injection of noise. In addition, by comparing the diagnostic accuracy of ones with single-channel and multi-channel information fusion under the optimal noise level in both cases, it is found that one with multi-channel information fusion shows an average of 3.20% increase than the single-channel one.

Fault diagnosis method of switch machine based on adaptive neural fuzzy network

Aiming at the problem of a large number of switch machines, harsh working environment and high frequency of equipment failures caused by many factors, in order to accurately diagnose railway switch machine faults, a switch machine fault diagnosis method based on adaptive neural fuzzy network (ANFIS) optimized by dynamic weight particle swarm (DPSO) algorithm is proposed by analyzing the vibration signals generated during the operation of the switch machine. Firstly, the working vibration signal is decomposed into several intrinsic mode functions (IMFs) and selected by using the ensemble empirical mode decomposition (EEMD) algorithm; then, the feature values of IMFs are extracted by using the improved time domain multiscale scatter extraction (ITMDE) algorithm, and then input into the optimized ANFIS model to learn and realize fault diagnosis; finally, the method is compared and analyzed with various diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose switch machine faults, which has certain reference significance for intelligent diagnosis of switch machine faults and related research in the future.

Anti-forgetting source-free domain adaptation method for machine fault diagnosis

Unsupervised domain adaptation methods have made significant progress in the field of machine fault diagnosis. However, these methods generally assume the accessibility of source domain data during the cross-domain transfer phase. In practice, this assumption is often impractical due to data privacy requirements and the burden of data transmission and storage. Additionally, a key challenge lies in preventing the forgetting of source domain knowledge while performing cross-domain diagnosis in a source-free scenario. To address these issues, this paper proposes an anti-forgetting source-free domain adaptation method for machine fault diagnosis. Specifically, a prototype-based pseudo-label generation strategy, combined with a nearest-neighbor constraint, is employed to deeply explore diagnosis information in the unlabeled target domain data. Then, a fast nuclear-norm maximization constraint is introduced to reduce the density of target domain samples near the classification boundary, thereby decreasing the probability of misdiagnosis. Furthermore, a weighted Fisher regularization is designed to retain the diagnosis information of healthy states inherent in the source domain, thus mitigating information forgetting. Finally, comprehensive experiments are conducted to validate the superiority of our method in source-free cross-domain diagnosis from multiple perspectives, while also demonstrating its anti-forgetting properties.

Domain generalization for machine compound fault diagnosis by Domain-Relevant Joint Distribution Alignment

In machine fault diagnosis, domain generalization methods have gained significant attention due to their advantage in non-requirement of priori target domain distribution. However, they pose great challenges in domain-relevant feature learning and incurs inferior unseen-domain classification when large domain divergence exists. To address this issuse, we propose a novel distribution alignment strategy named DRJDA (Domain-Relevant Joint Distribution Alignment) that matches the domain-joint distribution and domain-relevant distribution for domain generalization fault diagnosis. Specifically, the α-PE divergence is employed to minimize the distribution discrepancy, which is demonstrated to be explicitly derived as the maximum value of a quadratic function. Additionally, a parameter-free plug-and-play data augmentation module that performs feature-level instance mixture and style transfer to increase the generalization ability. Finally, data from the China Light-duty Vehicle Test Cycle (CLTC) tests are used as case studies, and the experiments carried out across 14 different domains prove the proficiency of the proposed DRJDA in learning domain-invariant feature when significant domain divergence exists, indicating its remarkable potential in compound fault diagnosis for industrial machinery.

Dual Weighted-Class Adversarial Network for Rotary Machine Fault Diagnosis Using Multisource Domain with Class-Inconsistent Data

Dual Weighted-Class Adversarial Network for Rotary Machine Fault Diagnosis Using Multisource Domain with Class-Inconsistent Data

Optimal weighted multi-scale entropy-energy ratio feature for machine fault diagnosis

Identifying the sensitive characteristics of mechanical equipment components is crucial for effective fault diagnosis. However, focusing solely on a specific feature at a single time scale fails to comprehensively capture the device’s operational state. Inspired by the concept of multi-scale analysis and recognizing the complementary strengths of permutation entropy (PE) and root mean square (RMS) in fault characterization, we propose a novel feature called the Optimal Weighted Multi-Scale Entropy-Energy Ratio (OWMEER). This feature aims to enhance fault characterization by optimally combining the strengths of PE and RMS, thereby providing a more comprehensive assessment of the device’s condition. The effectiveness and superiority of OWMEER in fault characterization have been validated through experimental data, including both public and self-test datasets, when combined with the commonly used pattern recognition methods such as random forest (RF) and support vector machine (SVM). The results demonstrate that using OWMEER as a fault feature not only yields better results than using the original features RMS and PE, but also maintains strong diagnostic performance across different classifiers and datasets.

Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

Multi-sensor time-series data at different locations contains not only temporal correlation information but also spatial correlation information which is treasure for machine fault diagnosis. Existing graph construction methods mainly apply different data analysis methods to connect nodes and edges. Few works, however, consider the location of the sensor itself and temporal correlation information of multi-sensor time-series data. To mine the relationship between spatial information and temporal information, the multi-sensor temporal-spatial graph is constructed in this paper. Hereinto, the different data points of multi-sensor are severed as different nodes which represents the spatial feature information. The temporal information is contained between different nodes of the same sensor. Moreover, an empirical mode decomposition graph convolution network (EGCN) is proposed to extract the feature. Specifically, the traditional graph convolution operator is changed to empirical mode decomposition which can decompose the input features into multiple intrinsic modal features to achieve adaptive feature extraction and improve the representation capability of the network. Finally, the different fault types can be classified by fully connected layers. Experiments from different test rigs demonstrate that the proposed method achieves a diagnostic accuracy exceeding 99 % under limited fault samples.

Rainforest: A three-stage distribution adaptation framework for unsupervised time series domain adaptation

Solving the unsupervised domain adaptation (UDA) task in time series is of great significance for practical applications, such as human activity recognition and machine fault diagnosis. Compared to UDA for computer vision, UDA in time series is more challenging due to the dynamics of time series data and the complex dependencies among different time steps. Existing UDA methods for time series fail to adequately capture the temporal dependencies, limiting their ability to learn domain-invariant temporal patterns. Furthermore, most UDA methods only focus on distribution adaptation on the backbone network without considering how the classifier adapts to the data distribution of the target domain. In this paper, we propose Rainforest, a three-stage UDA framework for time series. We first pre-train the backbone network through a self-supervised method called bidirectional autoregression, so that the model can comprehensively learn the temporal dependencies in time series. Next, we propose a novel meta-learning-based distribution adaptation method to achieve the joint alignment of the global and local distributions while encouraging the model to adaptively reduce the temporal dynamic differences among different domains. Finally, we design a pseudo-label-guided fine-tuning strategy to help the classifier estimate the data distribution of the target domain more accurately. Extensive experiments on four real-world time series datasets show that our Rainforest outperforms state-of-the-art methods, with an average improvement of 2.19% in accuracy and 2.41% in MF1-score.

A General and Efficient Fault Diagnosis Approach Using Hyperdimensional Computing Enhanced by Local Feature Extraction

Abstract Ensuring the safe and reliable operation of industrial equipment is of great importance, and accordingly, machine fault diagnosis has attracted considerable attention. Recently, various deep learning-based fault diagnosis models have been widely developed due to the powerful ability of feature learning and nonlinear modeling of deep learning models, and these methods heavily rely on large amounts of data and high memory and resources. However, it is difficult to obtain enough data in practical applications, thus leading to the unsatisfactory performance of deep learning-based models and high cost. In this paper, we propose a new and efficient fault diagnosis approach based on Hyperdimensional computing (HDC), which can learn high-dimensional, random, and holographic distributed representations of input features. It has the advantages of single training, low training cost, and does not require a large amount of data. Therefore, we apply HDC to the field of few-shot fault diagnosis for the first time, and propose a simple and efficient approach for few-shot fault diagnosis based on hyperdimensional computing with local feature extraction(L-HDC). Instead of using original data, the model firstly extracts the local features of original data by hand-designed features, and then carries out HDC-encoding, training, and test classification. Through the comparison of various models and the analysis of various data, the effectiveness of our proposed model is verified. In the case of few-shot learning, the recognition accuracy is as high as 90\% and the training time of ordinary HDC model is 5-10 times that of L-HDC model, which is superior to other models.

Vibration and acoustic signal-based bearing fault diagnosis in CNC machine using an improved deep learning

Vibration and acoustic signal-based bearing fault diagnosis in CNC machine using an improved deep learning

Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation

Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement of small memory and fast-precise prediction in practical applications. To address these challenges, a lightweight pyramid attention residual network (PARNet) is proposed in this paper. First, an efficient pyramid squeeze convolution module is proposed to capture multi-scale information from raw signals. Then, a soft squeeze-and-excitation attention is designed to establish long-range channel dependency, and fuse features of different scales. Based on the above improvements, a pyramid attention residual block is proposed as basic feature learning unit, and a concise CNN-based model PARNet is built using PAR block. Extensive experiments in two case studies are conducted to validate the effectiveness and adaptability of PARNet under different speed variation conditions. The test results demonstrate the superiority of the proposed method for machine fault diagnosis and reliability assessment in terms of recognition accuracy and model parameter complexity, compared with other advanced diagnostic methods.

Machine Fault Diagnosis: Experiments with Different Attention Mechanisms Using a Lightweight SqueezeNet Architecture

As artificial intelligence technology progresses, deep learning models are increasingly utilized for machine fault classification. However, a significant drawback of current state-of-the-art models is their high computational complexity, rendering them unsuitable for deployment in portable devices. This paper presents a compact fault diagnosis model that integrates a self-attention SqueezeNet architecture with a hybrid texture representation technique utilizing empirical mode decomposition (EMD) and a gammatone spectrogram (GS) filter. In the model, the dominant signal is first isolated from the audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD, and subsequently, the dominant signals are transformed into 2D texture maps using the GS filter. These generated texture maps feed as input into the modified self-attention SqueezeNet classifier, featuring reduced model width and depth, for training and validation. Different attention modules were tested in the paper, including the self-attention, channel attention, spatial attention, and convolutional block attention module (CBAM). The models were tested on the MIMII and ToyADMOS datasets. The experimental results demonstrated that the self-attention mechanism with SqueezeNet achieved an accuracy of 97% on the previously unseen MIMII and ToyADMOS datasets. Furthermore, the proposed model outperformed the SqueezeNet attention model with other attention mechanisms and state-of-the-art deep architectures, exhibiting a higher precision, recall, and F1-score. Lastly, t-SNE is applied to visualize the features of the self-attention SqueezeNet for different fault classes of both MIMII and ToyADMOS.

ThermalTrend: a tool for organising and analysing thermal images

Infrared thermography (IRT), as a non-contact condition monitoring (CM) technique, measures the temperature distribution of machines and components to detect anomalous thermal behaviour. It is usually considered as a complementary technique and nowadays is used in a wide variety of industrial applications. In this paper, two commonly used criteria to assess the condition of machines based on the infrared thermography technique, ie absolute and relative temperature criteria, are represented. Then, the importance of the temperature trend in condition monitoring and fault diagnosis of machines and components is described. The ThermalTrend software, which was developed in the Condition Monitoring Unit, Smelting Department, Sarcheshmeh Copper Complex in Rafsanjan, Iran, is introduced and its most important and unique features are discussed. This software package helps thermographers to organise thermal images and provides the temperature trends of machines and components that are under regular monitoring. Finally, some industrial case studies on the usefulness and applicability of the infrared thermography technique and the ThermalTrend software are reported.

Investigations on interpretable nuclear-grade hydraulic damper monitoring and diagnosis from multi-source high-dimensional profile data

Condition monitoring and fault diagnosis is important to ensure smooth machine operation, in which the use of multi-source high-dimensional profile data composed of multiple physical signals collected by multiple sensors for machine health monitoring and fault diagnosis has not been fully explored. In this paper, interpretable monitoring and diagnostic methods are proposed to analyze multi-source high-dimensional profile data and identify equipment operational status. Firstly, polynomials of segmenting profiles as well as a Lasso model are established to obtain sparse features. Then, for offline data with historical fault labels, an optimal linear hyperplane classifier is constructed to interpretably study the impact of different features on a decision. For online data with only normal samples, constructing an optimal hypersphere enables the measurement of operational status. Large-scale nuclear-grade hydraulic damper profile data are used to demonstrate how the proposed interpretable models work and show their physical interpretation for monitoring and fault diagnosis.

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine.

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model's optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.

Machine Learning Approaches for Fault Detection and Diagnosis in Electrical Machines: A Comparative Study of Deep Learning and Classical Methods

Fault detection and diagnosis in electrical machines are crucial for ensuring their safe and reliable operation. In recent years, machine learning techniques have emerged as powerful tools for addressing this challenge, offering the potential for more accurate and efficient fault detection and diagnosis compared to traditional methods. Among these techniques, deep learning has gained significant attention due to its ability to automatically learn relevant features from raw data. However, the performance of deep learning models in this domain has not been extensively compared to classical methods. This paper presents a comparative study of deep learning and classical methods for fault detection and diagnosis in electrical machines. The study evaluates the performance of various machine learning algorithms, including deep neural networks, support vector machines, decision trees, and ensemble methods, in detecting and diagnosing faults such as stator winding faults, rotor faults, and bearing faults. The experimental evaluation is conducted using real-world datasets obtained from electrical machines in industrial settings. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each approach in detecting and diagnosing faults accurately and efficiently. The results of the study indicate that deep learning approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), outperform classical methods in terms of fault detection and diagnosis accuracy. These deep learning models demonstrate the ability to automatically extract informative features from raw sensor data, enabling them to effectively identify subtle patterns indicative of faults. The study investigates the interpretability of deep learning models compared to classical methods, examining the extent to which the models can provide insights into the underlying causes of faults. While deep learning models typically operate as black boxes, techniques such as layer-wise relevance propagation (LRP) are employed to enhance their interpretability and facilitate the identification of relevant features contributing to fault detection and diagnosis. This comparative study provides valuable insights into the strengths and limitations of deep learning and classical methods for fault detection and diagnosis in electrical machines, offering guidance for practitioners and researchers in selecting appropriate approaches for their specific applications.

Few-shot fault diagnosis of switch machine based on data fusion and balanced regularized prototypical network

The turnout switch machine (TSM) is the critical signal equipment of the interlocking system, which directly affects the efficiency and safety of rail transit. However, the incomplete feature information of single-source data and the scarcity of fault data in real scenes make the existing methods unable to obtain satisfactory diagnostic results. Given the above issues, a few-shot fault diagnosis method based on data fusion and balanced regularized prototypical network (BRPN) is proposed. Firstly, a multi-signal adaptive fusion strategy is proposed to adaptively fuse three currents of the TSM to achieve a comprehensive and accurate expression of fault information. Secondly, a multi-level feature fusion network is designed to enhance the feature extraction ability of the fusion current signal. Furthermore, a prototype distribution balanced regularized strategy is proposed to balance the distribution differences between prototype interclass, so as to improve the diagnostic performance of the BRPN. Finally, the proposed BRPN model is verified by data fusion experiments and different small sample fusion datasets. Compared with other methods, the proposed method shows satisfactory diagnostic results in 1-shot and 5-shot experiments with two conversion processes, and the highest accuracy of fault diagnosis reaches 99.60%, which can provide a solution for solving the scarce fault samples of switch machines in a real-world situation.

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

LiteFormer: A Lightweight and Efficient Transformer for Rotating Machine Fault Diagnosis

LiteFormer: A Lightweight and Efficient Transformer for Rotating Machine Fault Diagnosis