Showing papers on "Fault detection and isolation published in 2020"

A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.

Abstract: Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its effectiveness in handling industrial process data, which are often with high nonlinearities and strong correlations. However, the valuable information in the raw data may be filtered with the layer-wise feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is proposed to fully exploit useful information in the raw data, in which raw data is combined with the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic characteristics of process data into consideration. Finally, to test the performance of the proposed method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.

A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains

Abstract: High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. The first objective of this paper is to present a comprehensive review on the fault detection and diagnosis (FDD) techniques for high-speed trains. The second purpose of this work is, motivated by the pros and cons of the FDD methods for high-speed trains, to provide researchers and practitioners with informative guidance. Then, the application of FDD for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years. Finally, the challenges and promising issues are speculated for the future investigation.

Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

Abstract: Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very difficult to meet the real-time requirement of online FDD applications. Since transfer learning can solve different but similar problems in the target domain efficiently and effectively with the knowledge learned from the source domain, this paper proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework. The TCNN framework is made up of an online CNN based on LeNet-5 and several offline CNNs with a shallow structure. First, time-domain signal data are converted into images that contain abundant fault information and are suitable as the input of CNN. Then, the online CNN is constructed to automatically extract representative features from the converted images and classify faults. Finally, in order to improve the real-time performance of the online CNN, several offline CNNs are also constructed and pretrained on related data sets. By directly transferring the shallow layers of the trained offline CNNs to the online CNN, the online CNN can significantly improve the real-time performance and successfully address the issue of achieving the desired diagnostic accuracy within limited training time. The proposed method is validated on two bearing data sets and one pump data set, respectively. The prediction accuracy of the proposed method using three data sets are 99.88%, 99.13%, and 99.98%, respectively. The experimental results also indicate that the improvement of accuracy is 19.21% for the motor bearing case, 29.82% for the rolling mill bearing case, and 33.26% for the pump case during the early stage of learning.

A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

Abstract: Rooftop and building-integrated distributed photovoltaic (PV) systems are emerging as key technologies for smart building applications. This paper presents the design methodology, mathematical analysis, simulation study, and experimental validation of a digital twin approach for fault diagnosis. We develop a digital twin that estimates the measurable characteristic outputs of a PV energy conversion unit (PVECU) in real time. The PVECU constitutes a PV source and a source-level power converter. The fault diagnosis is performed by generating and evaluating an error residual vector, which is the difference between the estimated and measured outputs. A PV panel-level power converter prototype is built to demonstrate how the sensing, processing, and actuation capabilities of the converter can enable effective fault diagnosis in real time. The experimental results show detection and identification of ten different faults in the PVECU. The time to fault detection (FD) in the power converter and the electrical sensors is less than 290 $\mu$ s and the identification time is less than 4 ms. The time to FD and identification in the PV panel are less than 80 ms and 1.2 s, respectively. The proposed approach demonstrates higher fault sensitivity than that of existing approaches. It can diagnose a 20% drift in the electrical sensor gains and a 20% shading of a solar cell in the PV panel.

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Abstract: A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset.

Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions

Abstract: Intelligent fault detection and diagnosis, as an important approach, play a crucial role in ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most important components in the rotating machinery. In real industries, it is common to face that the issues of severe data imbalance and distribution difference since the number of fault data is small and the equipments frequently change the working conditions according to the production. To accurately and automatically identify the conditions of rolling bearings, a normalized convolutional neural network is proposed for the diagnosis of different fault severities and orientations considering data imbalance and variable working conditions. First, the batch normalization is adopted as a novel application to eliminate feature distribution difference, which is the prerequisite for ensuring generalization ability under different working conditions. Then, a special model structure is established and the overall performances of the proposed model are optimized by iterative update, which combines the exponential moving average technology. Finally, the proposed model is applied to the fault diagnosis under different data imbalance cases and working conditions. The effectiveness of the proposed method is verified based on two popular experiment dataset, and the diagnosis performance is widely evaluated in different scenarios. Comparisons with other commonly used methods and related works on the same dataset demonstrate the superiority of the proposed method. The results show that the proposed method has excellent diagnosis accuracy and admirable robustness, and also has sufficient stability on the data imbalance.

Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains.

Abstract: Stator/rotor winding faults are the common faults in squirrel-cage induction motor systems, which motivates the study of incipient fault detection and isolation (IFDI) to improve the safety and reliability of CRH (China Railway High-speed) trains. In this paper, a dynamic model for squirrel caged induction motor in d-q coordinate system is established firstly, further, the models and characteristics of incipient broken-rotor-bar fault and turn-to-turn short fault are analyzed. After that, a novel robust diagnosis design is proposed for the possible incipient stator/rotor winding faults. Experimental analyses show that the detection and isolation scheme designed in this paper provides high sensitivity and accurate isolation to incipient winding faults.

Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach

Abstract: This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. First, the continuous-time FSMJSs model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Second, the IT2 fuzzy semi-Markov mode-dependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using the Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with $H_{\infty }$ performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system that is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method.

A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis

Abstract: With the advent of the era of big data, buildings have become not only energy-intensive but also data-intensive. Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems. This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain. In general, data mining technologies can be classified into two categories, i.e., supervised data mining technologies and unsupervised data mining technologies. In this field, supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis. And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis. Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods. Based on this review, suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.

A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance

Abstract: Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.

Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

Abstract: Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE–EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.

A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

Abstract: Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high impedance faults, low location mismatch, and the presence of Maximum Power Point Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this regard, there have been several attempts made by various researchers to identify PV array faults. However, most of the previous work has focused on fault detection and classification in only a few faulty scenarios. This paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults. An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults - both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS - on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. We generate a consistent dataset over which to compare ours and previous approaches, to make for the first (to the best of our knowledge) comprehensive and meaningful comparative evaluation of fault diagnosis. It is observed that the proposed method involving fine-tuned pre-trained CNN outperforms existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also highlights the importance of representative and discriminative features to classify faults (as opposed to the use of raw data), especially in the noisy scenario, where our method achieves the best performance of 70.45%. We believe that our work will serve to guide future research in PV system fault diagnosis.

Structural Health Monitoring for Advanced Composite Structures: A Review

Abstract: Condition-based maintenance refers to the installation of permanent sensors on a structure/system. By means of early fault detection, severe damage can be avoided, allowing efficient timing of maintenance works and avoiding unnecessary inspections at the same time. These are the goals for structural health monitoring (SHM). The changes caused by incipient damage on raw data collected by sensors are quite small, and are usually contaminated by noise and varying environmental factors, so the algorithms used to extract information from sensor data need to focus on sensitive damage features. The developments of SHM techniques over the last 20 years have been more related to algorithm improvements than to sensor progress, which essentially have been maintained without major conceptual changes (with regards to accelerometers, piezoelectric wafers, and fiber optic sensors). The main different SHM systems (vibration methods, strain-based fiber optics methods, guided waves, acoustic emission, and nanoparticle-doped resins) are reviewed, and the main issues to be solved are identified. Reliability is the key question, and can only be demonstrated through a probability of detection (POD) analysis. Attention has only been paid to this issue over the last ten years, but now it is a growing trend. Simulation of the SHM system is needed in order to reduce the number of experiments.

Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks

Abstract: Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%–110% of $P_{n}$ ) and distribution network voltage levels (±10% of $U_{n}$ ). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

A Distributed Cyber-Attack Detection Scheme With Application to DC Microgrids

Abstract: DC microgrids often present a hierarchical control architecture, requiring integration of communication layers. This leads to the possibility of malicious attackers disrupting the overall system. Motivated by this application, in this article, we present a distributed monitoring scheme to provide attack-detection capabilities for linear large-scale systems. The proposed architecture relies on a Luenberger observer together with a bank of unknown-input observers at each subsystem, providing attack detection capabilities. We describe the architecture and analyze conditions under which attacks are guaranteed to be detected, and, conversely, when they are stealthy . Our analysis shows that some classes of attacks cannot be detected using either module independently; rather, by exploiting both modules simultaneously, we are able to improve the detection properties of the diagnostic tool as a whole. Theoretical results are backed up by simulations, where our method is applied to a realistic model of a low-voltage DC microgrid under attack.

A Review on Fault Detection and Process Diagnostics in Industrial Processes

Abstract: The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.

Observer-Based Asynchronous Fault Detection for Conic-Type Nonlinear Jumping Systems and its Application to Separately Excited DC Motor

Abstract: This work investigates the observer-based asynchronous fault detection problem for a class of nonlinear Markov jumping systems. The conic-type nonlinearities hold such a restrictive condition that locates in a known hypersphere with an undefined centre. In order to guarantee the observer modes run synchronously with the system modes, we introduce a hidden Markov model to deal with this difficulty. Utilizing $H_\infty $ and $H_\_{}$ performance index, a multi-targets strategy of asynchronous fault detection problem is formulated. Via linear matrix inequality, sufficient conditions for the presence of the asynchronous fault detection observer are derived respectively. Then an asynchronous fault detection algorithm is formulated. Finally, the application of dynamic equivalent circuit of separately excited DC motor with three cases is employed to illustrate that the devised asynchronous fault detection observer is able to detect the faults after the appearances in the absence of any incorrect alarm.