Class Of Faults Research Articles

Energy efficient fault detection and classification using hyperparameter-tuned machine learning classifiers with sensors

This paper presents a novel approach in which hyperparameter-tuned Machine Learning (ML) classifiers with Optuna is used for fault detection and classification over a power transmission line. In this paper, popular ML models Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and Light Gradient Boosting Machine (LGBM) are used for fault detection and classification. This study uses a two-layer approach for fault detection and classification. An electrical utility and data simulation provided the PMU measurement and recorded data from a simulated grid. The faults had varied impedances and included various fault classes at distinct line locations. The optimal feature from 3-phase current and voltage signals is extracted using Pearson correlation, recursive feature elimination, and univariate feature (t-test) methods. The synthetic minority class oversampling technique (SMOTE) was used to address the issue of imbalanced data. Hyperparameters of the evaluated LGBM classifier is trained with the Optuna. The performance of the proposed classifier is measured in terms of the accuracy, precision, Recall and F1-score metrics. The proposed method outperformed the conventional ML methods.

A new class of fault recognition method for wind turbine systems based on deep learning

SummaryConventional fault recognition algorithms can only recognize the classes of faults that have appeared in wind turbine systems. However, if a new category of faults appears, the traditional algorithm can only misclassify it into the class of pre‐existing faults. In this paper, a new class fault recognition method based on deep learning is proposed. Firstly, the initialized model is built using known fault data, including detectors and classifiers. Secondly, the new class fault data detected by the detectors are put into the cache, and when the cache overflows, the new class faults are augmented with data using generative adversarial networks. Finally, the new class fault data and the generated data are added to the initial training set, and the structure and weights of the initialized model are updated using the new training set. In this way, the purpose of recognizing the new class of faults is achieved. The experimental results show that the proposed detection algorithm can effectively detect new class faults and the model can efficiently solve the problem of the new class of fault recognition after data augmentation.

Resilient Corrective Control of Asynchronous Sequential Machines Against Intermittent Loss of Actuator Outputs.

This article proposes a resilient corrective control scheme for input/state asynchronous sequential machines (ASMs) against a class of actuator faults in which certain actuator outputs cannot be generated temporarily. We first present a mathematical formulation to describe the reachability of the controlled ASM damaged by the intermittent loss of actuator outputs. Based on the mathematical formulation, we address the existence condition and design procedure for a state-feedback corrective controller and a diagnoser that achieve resilience, that is, to make the closed-loop system exhibit normal input/state behaviors despite the intermittent loss of actuator outputs. To validate the applicability of the proposed concept and methodology, the closed-loop system of a practical asynchronous digital system is implemented on a field-programmable gate array (FPGA) and experimental verifications are provided.

Novel joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis

Traditional deep learning fails to identify new faults when the number of faulty samples is limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not fully account for the differences in the distribution of faults across domains between training data and new fault classes, which limits further performance improvement of the diagnosis model. In this study, we propose a new joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis. First, a hybrid attention is used to enhance the feature extraction ability of the model and suppress redundant features. The proposed joint transfer function is used to align the corresponding subdomains of the source and target domains in small samples. Finally, cross-domain few-shot fault diagnostics is implemented using a modified fine-grained metric network. The proposed joint transfer fine-grained metric network outperformed other common meta-learning fault diagnosis methods on three mechanical device datasets.

Novel Ensemble Domain Adaptation Methodology for Enhanced Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets

This paper proposes a novel methodology for enhancing multi-class classification accuracy in fault diagnosis problems among domains with highly-connected fleets of assets using time series data. The approach involves appending specially tailored models to an initial model and incorporating domain adaptation techniques to account for domain variations. The methodology is demonstrated through a case study on fault diagnosis of a fleet of hydraulic rock drills, which presents challenges due to variations in sensor data between different fault classes and individual machines. Results show significant improvements in classification accuracy, both in validation and testing, upon employing ensemble models and applying domain adaptation. While the study is limited to one case study, it lays the groundwork for exploring the applicability of the proposed methodology in other contexts.

Cross-Component Transferable Transformer Pipeline Obeying Dynamic Seesaw for Rotating Machinery with Imbalanced Data.

Due to the lack of fault data in the daily work of rotating machinery components, existing data-driven fault diagnosis procedures cannot accurately diagnose fault classes and are difficult to apply to most components. At the same time, the complex and variable working conditions of components pose a challenge to the feature extraction capability of the models. Therefore, a transferable pipeline is constructed to solve the fault diagnosis of multiple components in the presence of imbalanced data. Firstly, synchrosqueezed wavelet transforms (SWT) are improved to highlight the time-frequency feature of the signal and reduce the time-frequency differences between different signals. Secondly, we proposed a novel hierarchical window transformer model that obeys a dynamic seesaw (HWT-SS), which compensates for imbalanced samples while fully extracting key features of the samples. Finally, a transfer diagnosis between components provides a new approach to solving fault diagnosis with imbalanced data among multiple components. The comparison with the benchmark models in four datasets proves that the proposed model has the advantages of strong feature extraction capability and low influence from imbalanced data. The transfer tests between datasets and the visual interpretation of the model prove that the transfer diagnosis between components can further improve the diagnostic capability of the model for extremely imbalanced data.

Slip-rates and time recurrences of the seismogenic Sant’Eufemia normal fault (SE Tyrrhenian Sea), a multiscale and multidisciplinary approach

Time recurrences and slip rates of paleoearthquakes along offshore seismogenic faults represent one of the key challenges that the scientific community is facing off. Here we provide an integrated study combining geophysical, sedimentological and geochemical data in order to define slip rates and time recurrences of the Sant’Eufemia normal Fault (SE Tyrrhenian Sea). We have documented the paleo-events associated to this geological structure and reconstructed the paleoenvironmental history. From post-LGM (ca. 20,484 yr BP) to Present, four main events (EQ1, EQ2, EQ3, and EQ4) and the historical 1905 Calabria earthquake have been recognized. Time recurrence ranges from 4.68 ± 0.94 to 7.322 ± 1.84 ky (mean value of 5.4 ky), while the co-seismic vertical displacement ranges from 0.21 to 1.085 m (mean value of 0.68 m), indicating that slip rates change along the fault plane moving from the central part to the lateral edges. In this work we have defined a new class of active fault, here named “ultra-slow”, that allowed to extend the range of time recurrences previously provided for the main earthquakes affecting the Southern Apennines. Phenomena that may be related with the oceanic slab sinking beneath the Calabrian arc.

A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems.

Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement

Deep-learning-based identification and diagnosis of faults in industrial assets has received much attention. It does not need a deep understanding of the target domain or complex signal-processing techniques for time-consuming feature identification and selection. The vast majority of approaches developed in this field rely on public data recorded in laboratory conditions with high-performance measurement equipment. Moreover, primarily vibration data is used for condition monitoring tasks, which is particularly sensitive to typical error patterns in rotating machinery. These conditions are difficult to maintain in industrial environments since using high-performance measurement systems would not be economically feasible. This paper demonstrates that modern deep learning can achieve extraordinary fault detection results with industrial standard low-cost measurement systems. We determine measurements taken from the industrial-sensory equipment that potentially capture the system’s fault patterns. Hereafter, we apply traditional failure detection and identification techniques to evaluate the suitability of the taken measurements for bearing fault detection. These techniques apply envelope analysis to reveal the fault patterns in the signals and a support vector machine to separate the fault classes. We can conclude that the used low-cost sensory equipment is capable to capture meaningful, fault-describing patterns. To improve the failure detection performance we are investigating a 2D, as well as 1D, convolutional neural network approach to identify the error patterns and classify the respective errors. We compare the deep-learning-based methods with the traditional methods. Furthermore, we assess which inverter signal carries the largest fault-describing information content. Experimental results indicate that the proposed deep learning methods outperform traditional fault diagnosis methods, hence, demonstrating the effectiveness in an industrial condition monitoring application.

Variational autoencoder based on distributional semantic embedding and cross-modal reconstruction for generalized zero-shot fault diagnosis of industrial processes

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. To solve this problem, a generalized zero-shot fault diagnosis model for industrial processes based on distributional semantic embedding and cross-modal reconstruction variational autoencoder (DSECMR-VAE) is proposed. DSECMR-VAE uses two variational autocoders (VAEs) to encode the features from two different modalities of fault samples and fault attribute semantic vectors into latent variables, and then uses the latent variables to generate fault samples belonging to the seen and unseen fault classes. In addition, a Barlow matrix is designed specifically for the distribution parameters of the latent variables, this matrix is utilized to measure the consistency between the distribution of fault samples and fault attribute semantic vectors. Subsequently, cross-modal reconstruction is performed on VAE. Cross-modal reconstruction uses the input from different modalities to reconstruct the current input, which can fully combine the information from different modalities. Finally, a classifier is trained based on the obtained latent variables to realize the generalized zero-shot fault diagnosis. Two case studies demonstrate the effectiveness and superiority of the proposed model for zero-shot and generalized zero-shot fault diagnosis.

The Reform of Equipment Fault Class Course Based on Action-Oriented

This paper adapts to the needs of teaching, insists on the problem-oriented according to the reality of the fault course, points out the gap between the current failure course and the actual combat requirement, and takes the goal-oriented as the traction, and puts forward the basic strategy of organizing the fault course teaching according to the student's psychological development course, which can provide reference for the follow-up development of the fault course.

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

AbstractThe overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block‐based attention mechanism stacked autoencoder (MB‐AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self‐attention is used to focus on the information of each block itself. Cross‐attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee‐Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB‐AMSAE algorithm in process monitoring.

Fault diagnosis of plunger pump based on audio signal combined with meta-transfer learning

To solve the problems of insufficient samples and weak fault features of audio signals in the fault diagnosis of plunger pump, this paper proposes a fault diagnosis method of plunger pump based on audio signal combined with meta-transfer learning (MTL-PAFD). The method takes the audio signals of the plunger pump as samples, which are acquired by a single sensor. Through the Gammatone filter bank processing, the representation ability of the audio signal under strong noise interference is effectively improved. Then combined with meta-transfer learning, the few-shot fault diagnosis of plunger pump is realized. In addition, according to the actual needs of fault diagnosis of plunger pump, the test method of meta-transfer learning in fault diagnosis application is improved, which can process unknown fault classes adaptively. Experimental results show that MTL-PAFD has a fault diagnosis accuracy of 91.41% for seen classes. After fast adaptive learning, it can achieve an accuracy of 89.64% when identifying unseen fault classes.

Semantic-Consistent Embedding for Zero-Shot Fault Diagnosis

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that can not be collected in advance, which may restrict the performance of fault diagnosis methods. In this paper, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Barlow matrix</i> is designed to measure the consistency between the embedding of fault samples and the embedding of attribute vectors. The diagonal elements and the off-diagonal elements of the Barlow matrix encode the within-dimension consistency and between-dimension consistency of the cross-modal embeddings, respectively. Through optimizing the Barlow matrix to an identity matrix, SCE learns a significant space where the cross-modal embeddings have consistent representation while reducing the redundant components. Extensive experiments show that SCE gets significant superiority on the three-phase transmission system (26.9% gains) and the Tennessee Eastman process (15.5% gains). Moreover, SCE even gets competitive results with supervised learning methods. The code of SCE can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/htz-ecust/Semantic-consistent-Embedding</uri> .

PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals

AbstractEngines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time‐consuming and error‐prone. Automated fault type detection promises to reduce inter‐ and intra‐observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault‐type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low‐computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold‐out validation and 10‐fold cross‐validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real‐world applications.

Bilinear observer-based robust adaptive fault estimation for multizone building VAV terminal units

In this paper, we propose a novel bilinear observer-based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

Fault diagnosis of wind turbine power converter using intrinsic mode functions with relative energy entropy

PurposeThe purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.Design/methodology/approachThe most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.FindingsThe presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.Originality/valueThis study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.

Multiple faults diagnosis for an industrial robot fuse quality test bench using deep-learning

Diagnosing faults occurring in industrial equipment and monitoring its status is crucial for maintaining the continuous operation of the equipment. The conventional diagnostic approaches, such as model-based methods, need to model the dynamics of the physical process; however, obtaining an accurate dynamic model of complex interconnected industrial systems poses a significant challenge. Modern diagnostic methods based on data-driven using artificial intelligence models rely heavily on sensor data to perform fault detection and diagnosis. Conventional machine learning methods have low detection accuracy and rely on domain knowledge to extract meaningful features from data acquired from the equipment. On the other hand, deep learning models can automatically extract useful features from fault data with high diagnostic performance. This paper proposes a two-dimensional Convolutional Neural Network (2DCNN) diagnostic model to effectively improve the diagnostic accuracy of predicting faults on a tabular dataset with multiple fault classes. A simple sliding window approach is proposed to effectively transform and reshape the features in the dataset as inputs to the proposed model architecture. The method's feasibility was evaluated using data from an industrial robotic fuse quality test bench. Experimental results show high diagnostic performance with an average accuracy of 99.98% compared to conventional methods commonly used for diagnostics. Moreover, validation of the diagnostic model on the experimental dataset using raw multi-sensor fusion from a robot manipulator platform was carried out. The results demonstrate the model's outstanding diagnostic performance, with an average testing accuracy of 99.74% for four fault classes.

3D Scanner-Based Identification of Welding Defects-Clustering the Results of Point Cloud Alignment.

This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in the ISO 5817:2014 standard were evaluated. All defects were represented through CAD models, and the method was able to detect five of these deviations. The results demonstrate that the errors can be effectively identified and grouped according to the location of the different points in the error clusters. However, the method cannot separate crack-related defects as a distinct cluster.

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers.

The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen-Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model's performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.