Showing papers on "Fault detection and isolation published in 2022"
TL;DR: An adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed, aiming at the determination of cyclic frequency set estimation method based on autocorrelation function of morphological envelope and the validity of the method is verified.
107 citations
TL;DR: A new approach for fault detection and diagnosis in rotating machinery is proposed, namely: unsupervised classification and root cause analysis, and a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI).
94 citations
TL;DR: Based on the interval type-2 fuzzy (IT2F) approach, the authors investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities.
Abstract: Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.
88 citations
TL;DR: This paper provides a comprehensive review of blind deconvolution methods from history to state-of-the-art methods and finally to research prospects, as well as provides a survey and summarize the current progress of BDMs applied in machinery fault diagnosis.
72 citations
TL;DR: In this article , a new approach for fault detection and diagnosis in rotating machinery is proposed, which consists of three parts: feature extraction, fault detection, and fault diagnosis, and two tools for diagnosis are proposed, namely unsupervised classification and root cause analysis.
65 citations
TL;DR: A case study on FEMTO-ST datasets shows that the fine-tuned model is competent for incipient fault detection, outperforming other state-of-the-art methods.
59 citations
TL;DR: In this article , a fuzzy asynchronous fault detection filter (FAFDF) was proposed for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme.
Abstract: This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.
57 citations
TL;DR: An online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults.
Abstract: Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.
53 citations
49 citations
TL;DR: In this paper , the authors presented an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) model, which uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal.
Abstract: In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. In addition, Inception with ResNet v2 based feature extraction model is applied to generate high-level features. Besides, the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.
49 citations
TL;DR: A critical review of the existing internal combustion engine (ICE) modeling, optimization, diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) solutions for them is provided in this paper .
TL;DR: In this paper , the authors proposed a Digital Twin predictive maintenance framework of air handling unit (AHU) to overcome the limitations of facility maintenance management (FMM) systems now in use in buildings.
TL;DR: In this paper, a convolutional neural network-based arc detection model named ArcNet was proposed, which achieved an average runtime of 31 ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for realtime processing.
Abstract: AC series arc is dangerous and can cause serious electric fire hazards and property damage. This article proposed a convolutional neural network -based arc detection model named ArcNet. The database of this research is collected from eight different types of loads according to IEC62606 standard. The two most common types of arcs, including arcs from a loose connection of cables and those caused by the failure of the insulation, are generated in testing and included in the database. Using the database of raw current, experimental results indicate ArcNet can achieve a maximum of 99.47% arc detection accuracy at 10 kHz sampling rate. The model is also implemented in Raspberry Pi 3B for classification accuracy. A tradeoff study between the arc detection accuracy and model runtime has been conducted. The proposed ArcNet obtained an average runtime of 31 ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for real-time processing.
TL;DR: A novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed, which combines ANN and CCA from their respective principles, and the detailed gradient descent method derivation for the ANCA network is presented.
Abstract: In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.
TL;DR: In this article , a physics-informed hyperparameters selection strategy was proposed for the LSTM identification and subsequently the fault detection of gearboxes, where the key idea of the proposed strategy is to select hyper-parameters based on maximizing the discrepancy between healthy and physicsinformed faulty states, as opposed to minimizing validation mean squared error.
TL;DR: In this paper , a deep learning framework for linear systems with time-invariant parameters that identifies the presence and type of fault in sensor data, location of the faulty sensor and subsequently reconstructs the correct sensor data for fault detection, fault classification, and reconstruction is introduced.
TL;DR: In this paper , a self-supervised pre-training via contrast learning (SSPCL) is introduced to learn discriminative representations from unlabeled bearing datasets, and a specific architecture for SSPCL deployment on bearing vibration signals by presenting several data augmentations for 1D sequences.
TL;DR: In this article , the orthonormal subspace analysis (OSA) method is proposed to detect a fault and also judge whether the fault is KPI-related or not.
TL;DR: Wang et al. as mentioned in this paper combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network.
Abstract: In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.
TL;DR: In this article , a multi-head 1D Convolution Neural Network (1D CNN) was proposed to detect and diagnose six different types of faults in an electric motor using two accelerometers measuring in two different directions.
TL;DR: In this article , an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults.
Abstract: Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.
TL;DR: This study proposes an adversarial autoencoder (AAE) based process monitoring system which combines the advantages of a variational autoen coder and a generative adversarial network and enables the generation of features that follow the designed prior distribution.
Abstract: Deep learning has recently emerged as a promising method for nonlinear process monitoring. However, ensuring that the features from process variables have representative information of the high-dimensional process data remains a challenge. In this study, we propose an adversarial autoencoder (AAE) based process monitoring system. AAE which combines the advantages of a variational autoencoder and a generative adversarial network enables the generation of features that follow the designed prior distribution. By employing the AAE model, features that have informative manifolds of the original data are obtained. These features are used for constructing and monitoring statistics and improve the stability and reliability of fault detection. Extracted features help calculate the degree of abnormalities in process variables more robustly and indicate the type of fault information they imply. Finally, our proposed method is testified using the Tennessee Eastman benchmark process in terms of fault detection rate, false alarm rate, and fault detection delays.
TL;DR: In this article , a new combination of a correlative statistical analysis and the sliding window technique was proposed to detect incipient faults in thermal power plant process, which has been shown to have less calculation complexity.
Abstract: This article proposes a new combination of a correlative statistical analysis and the sliding window technique to detect incipient faults. Compared with the existing monitoring methods based on principal component and transformed component analyses, the combination fully uses the information from the process and quality variables. The sliding window, however, inevitably increases the computational burden due to the repeated window calculations. Therefore, a recursive algorithm is proposed in this article, which has been shown to have less calculation complexity. Furthermore, a randomized algorithm is proposed to determine the width of the sliding window. A numerical example and the thermal power plant process are presented to show the effectiveness and advantages of the proposed method.
TL;DR: In this article , the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems is reviewed and classified as two major approaches: knowledge-based and data-driven approaches.
Abstract: Faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.
TL;DR: Wang et al. as mentioned in this paper proposed a deep learning model with multirate data samples, which can extract features from the multi-rate sampling data automatically without expertise, thus it is more suitable in the industrial situation.
Abstract: Hydraulic systems are a class of typical complex nonlinear systems, which have been widely used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent fault diagnosis problem of hydraulic systems has received increasing attention for it can increase operational safety and reliability, reduce maintenance cost, and improve productivity. However, because of the high nonlinear and strong fault concealment, the fault diagnosis of hydraulic systems is still a challenging task. Besides, the data samples collected from the hydraulic system are always in different sampling rates, and the coupling relationship between the components brings difficulties to accurate data acquisition. To solve the above issues, a deep learning model with multirate data samples is proposed in this article, which can extract features from the multirate sampling data automatically without expertise, thus it is more suitable in the industrial situation. Experiment results demonstrate that the proposed method achieves high diagnostic and fault pattern recognition accuracy even when the imbalance degree of sample data is as large as 1:100. Moreover, the proposed method can increase about 10% diagnosis accuracy when compared with some state-of-the-art methods.
TL;DR: A novel condition monitoring and fault isolation system based on a covariate-adjusted preprocessing procedure to account for the various working conditions of the wind turbine, and constructs a global monitoring statistic based on all temperature variables contained in SCADA data.
Abstract: Condition monitoring of the wind turbine based on supervisory control and data acquisition (SCADA) data has attracted much attention in recent years. Nevertheless, there are some inherent challenges in SCADA data analysis, including the low sampling rate, time-varying working conditions of the wind turbine, and a lack of historical fault data. To solve these problems, this article develops a novel condition monitoring and fault isolation system. First, a covariate-adjusted preprocessing procedure is proposed to account for the various working conditions of the wind turbine. Next, we construct a global monitoring statistic based on all temperature variables contained in the SCADA data, with a view to monitoring the overall health status of the wind turbine. If an alarm is raised, we isolate the fault through a variable selection method without relying on expert knowledge or historical fault data. Simulation and real cases are provided to demonstrate the effectiveness of this system.
TL;DR: This work introduces a machine learning based technique that relies on satellite weather data and low-frequency inverter measurements for accurate fault diagnosis of PV systems, and demonstrates that this approach is sensitive to faults with a severity as small as 5 %.
01 Sep 2022
TL;DR: In this paper , a single-side canonical correlation analysis (SsCCA) is proposed to address the fault detection problem for industrial systems. But, it is not optimal in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal.
Abstract: Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships among variables. These assumptions may be improper in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal. With the aid of neural networks, this work proposes a new nonlinear counterpart called a single-side CCA (SsCCA) to enhance FD performance. The contributions of this work are four-fold: 1) an objective function for the nonlinear CCA is first reformulated, based on which a generalized solution is presented; 2) for the practical implementation, a particular solution of SsCCA is developed; 3) an SsCCA-based FD algorithm is designed for nonlinear systems, whose optimal FD ability is illustrated via theoretical analysis; and 4) based on the difference in FD results between two test statistics, fault diagnosis can be directly achieved. The studies on a nonlinear three-tank system are carried out to verify the effectiveness of the proposed SsCCA method.
TL;DR: In this paper , a five-step Chow's test-based computation procedure is proposed for condition monitoring of a wind turbine drivetrain with a nominal power of 2 MW using temperature-related SCADA data.
TL;DR: This research employs Deep Learning (DL) advances to develop a Recurrent Neural Network (RNN) model and a Long Short-Term Memory (LSTM) model to distinguish and locate Frequency Disturbance Events with significant accuracy.