Extended Invariant Risk Minimization for Machine Fault Diagnosis With Label Noise and Data Shift.

Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis.

Domain adaptation technologies have been successfully and increasingly applied to machine fault diagnosis. Regardless of the performance gains achieved, almost all approaches focus on interdomain adaptation and ignore the intradomain divergence of the target domain itself. Such intradivergence will cause the model not to adapt to all target domain data, resulting in inferior diagnosis outcomes. To address the abovementioned issue, a progressive adaptation method is proposed for machine fault diagnosis, diminishing the discrepancies from inter- to intradomain to realize more excellent performance. Specifically, smoothness-induced conditional adversarial learning is first deployed to solve the interdomain distribution discrepancy. After that, an adaptive screening mechanism is presented to split the target domain into an easy subset and a hard subset. Intradomain adaptation is then designed to further enhance diagnosis accuracy, in which task loss sharpness, data class imbalance, and label noise are considered simultaneously. The proposed intradomain adaptation is also plug-and-play and can effectively benefit most current approaches. We examine the proposed method on extensive fault diagnosis tasks and compare it with other competing methods from different perspectives, the comprehensive evidence demonstrates the efficacy and superiority of our approach.

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

Large margin-learning methodology from time-frequency maps and its physically interpretable weights for simultaneous machine health monitoring and fault diagnosis

Due to the real working conditions, the collected mechanical fault datasets are actually limited and always highly imbalanced, which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative model of conditional-deep convolutional generative adversarial network (C-DCGAN) and provide a study in detail. Deep convolutional generative adversarial network (DCGAN), based on traditional generative adversarial networks (GAN), introduces convolutional neural network into the training for unsupervised learning to improve the effect of generative networks. Conditional generative adversarial network (CGAN) is a conditional model obtained through introducing conditional extension into GAN. C-DCGAN is a combination of DCGAN and CGAN. In C-DCGAN, based on the feature extraction ability of convolutional networks, through the structural optimization, conditional auxiliary generative samples are used as augmented data and applied in machine fault diagnosis. Two datasets (Bearing dataset and Planetary gear box dataset) are carried out to validate. The simulation experiments showed that the improved performance is mainly due to the generated signals from C-DCGAN to balance the dataset. The proposed method can deal with imbalanced fault classification problem much more effectively. This model could improve the accuracy of fault diagnosis and the generalization ability of the classifier in the case of small samples and display better fault diagnosis performance.

Research on deep learning has been increasing in recent years in the field of fault diagnosis in rotary machine. However, compared to training data, real world data is collected from different system conditions and environments. Therefore, real world data has different datadistribution and various noise with the training data, leading to domain shift between data. Due to the problem mentioned above, deep learning often fails to apply on industrial data.Domain generalization is an emerging deep learning technique to generalize domain discrepancy. In this study, domain adversarial neural network (DANN)-based domaingeneralization is proposed for multi-label fault diagnosis of rotary machine. Frequency domain image data were generated via implementing short time fourier transform(STFT) to the sensor data collected from the test rig. Then, the features are utilized as training data to diagnosis multi-label fault via DANN-based domain generalization. Moreover, the upper boundary of rotating speed domain of the rotary machine where domain generalization can effectively diagnosis multi-label fault is suggested.

This paper study the wind turbine bearing vibration signal characterization and nonlinear transient characteristics, the transient signal characteristics were established based on Wigner-Ville spectrum entropy and support vector machine (SVM) extraction and fault diagnosis model and quantitative description of nonlinear characteristics of multi-scale method based on LMD sort of entropy, and the effectiveness of dynamic signal feature extraction and fault diagnosis method of the study, a typical experiment platform bearing bearing fault diagnosis experiment verified.

Recursive Actuator Fault Detection and Diagnosis for Emergency Landing of UASs

Automation of machine fault diagnosis is approached using an expert network which captures human expertise in symbolic form and is refined using historical performance data. A development environment for expert networks which draws from knowledge implicit in historical data to build and refine the expert network dynamically is presented. The testbed for the design of this development environment is fault diagnosis for gas chromatographs used in detecting contaminants in soil samples. The expert knowledge capture procedure for this testbed problem and its implementation in the G2 commercial expert system package were presented at AeroSense '95. The development environment for the fault diagnosis system includes several data-assisted methods which complement the expert knowledge embedded in the expert network. The first module presented, NetMaker, automatically constructs the network in G2 from an ASCH knowledge table file. NetMedic, the second module, is a data- assisted method which is used to confirm, refine, and augment expert knowledge in order to make the knowledge table more accurate. These tools form the foundation of the expert network development environment. The basis of the expert networks developed for machine fault diagnosis is the knowledge table, a matrix of signature symptoms and machine faults related by linguistic qualifiers. The knowledge table undergoes frequent revision due to refinements from the experts, data-enhanced knowledge from NetMedic, and improved symptom extraction algorithms. NetMaker satisfies the need to easily revise the knowledge tables and incorporate them seamlessly into the G2 expert network environment. NetMedic is used to improve machine fault diagnosis by suggesting alterations to the physical architecture of the knowledge table and the associated expert network, including several non-trainable parameters. This utility discovers relationships in the sample data using statistics from historical data. The experts may then incorporate new relationships in the expert knowledge as well as confirm existing knowledge. This approach preserves the ability to retrieve the expert knowledge from the modified network.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Reliable fault detection is essential for ensuring the safe and efficient operation of electrochemical energy storage systems, including lithium-ion batteries and transformer. However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise in training data, caused by sensor inaccuracies, ambiguous fault transitions, and imperfect labeling processes. This paper proposes a lightweight and effective kernel-based data rectification framework to improve the robustness of fault detection under noisy label conditions. The method identifies and discards low-density data points that are statistically more likely to be mislabeled, using kernel density estimation and a tunable data discarding strategy. The approach is computationally efficient, classifier-agnostic, and easily applicable to existing fault diagnosis pipelines. We evaluate the proposed method on two datasets: simulated lithium-ion battery voltage data under various fault scenarios, and transformer winding oscillation wave data under multiple winding fault conditions. The results demonstrate that the rectification framework significantly improves classification accuracy across both Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. Furthermore, the choice of discarding ratio is shown to be critical, with optimal performance achieved when the ratio is tuned close to the underlying noise level. These results highlight the potential of the proposed method to enhance the reliability of fault diagnosis in electrochemical energy storage systems. Future work will explore adaptive strategies to automatically optimize the rectification strength without requiring prior knowledge of the noise rate, and extend the framework to multi-sensor and multi-modal monitoring scenarios.

Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines

A novel industrial wireless sensor network (IWSN) for condition monitoring and fault diagnosis of electrical machines is presented, in which on-sensor fault diagnosis based on principal component analysis is explored to address the tension between the high system requirements of electrical machine monitoring and the resource constrained characteristics of IWSN sensor nodes. The prototype system is evaluated with a single phase induction motor monitoring system. Normal motor working conditions and two types of motor faults, ie. loose feet and mass imbalance, are monitored to validate the feasibility of the proposed system. The results show that using on-sensor fault diagnosis can reduce transmission data by 99.8%, decrease energy consumption, and prolong node lifetime from 106 to 153 h, an increase of 44%. The experimental results also indicate that the proposed approach has high fault diagnosis accuracy.

Mechanical equipment fault signals are mostly nonlinear and non-stationary signals. Processing these signals by Fourier transform and wavelet transform usually cannot obtain desired fault diagnosis results. In this paper, a machine fault diagnosis method based on Industrial Internet of Things (IIoT), industrial wireless sensor networks (IWSNs), Hilbert-Huang transform (HHT), and support vector machine (SVM) is proposed, in which HHT and SVM are used for fault feature extraction and fault diagnosis respectively. The proposed fault diagnosis approach by SVM is implemented and tested on the IWSN sensor node, while the fault feature extraction method using HHT is verified by MATLAB simulation. The effectiveness of the presented approach is evaluated by a set of experiments using bearing vibration data. The result indicates that the fault diagnosis accuracy of the presented method reaches 100% for four machine working conditions and 92% for five working conditions.

This research is concerned with the fault diagnosis for machines using neural networks. Generally, it is difficult to diagnose machine's fault by the conventional technique. In this research, two new fault diagnosis systems are proposed which one diagnoses a fault based on behavior of the object system, and another diagnoses a fault based on power spectrum of the object system. In the former, when an object system is a normal state, the system identification is performed by the neural networks. The diagnosis system detects a fault by finding the behavior's gap between the state of the real system and the identified normal one, and also the fault part is specified by fault diagnosis neural network. In the latter, neural network learns power spectrum of both the normal and fault states for the object. When a fault occurs, fault part is diagrnosed by fault diagnosis neural network based on power spectrum. Finally, through simulation and experiment, the effectiveness of proposed fault diagnosis systems is verified.

A graph neural network-based data cleaning method to prevent intelligent fault diagnosis from data contamination