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1 – 10 of over 3000
Article
Publication date: 19 March 2020

Xiaoling Li and Shuang shuang Liu

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is…

Abstract

Purpose

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.

Design/methodology/approach

The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.

Findings

The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.

Originality/value

The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 28 November 2022

Junyan Ma and Yiping Yuan

With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition…

Abstract

Purpose

With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection.

Design/methodology/approach

The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed.

Findings

Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type.

Originality/value

The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.

Details

Sensor Review, vol. 43 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 19 April 2022

D. Divya, Bhasi Marath and M.B. Santosh Kumar

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive…

1616

Abstract

Purpose

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

Design/methodology/approach

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Findings

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Originality/value

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 27 January 2022

Chen-Long Li, Chang-Shun Yuan, Xiao-Shuang Ma, Wen-Liang Chen and Jun Wang

This paper aims to provide a novel integrated fault detection method for industrial process monitoring.

96

Abstract

Purpose

This paper aims to provide a novel integrated fault detection method for industrial process monitoring.

Design/methodology/approach

A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output.

Findings

The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process.

Originality/value

The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.

Details

Assembly Automation, vol. 42 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 1 June 2020

Zhengquan Chen, Lu Han and Yandong Hou

This paper proposes a novel method of fault detection, which is based on H_/H∞ Runge–Kutta observer and an adaptive threshold for a class of closed-loop non-linear systems. The…

Abstract

Purpose

This paper proposes a novel method of fault detection, which is based on H_/H∞ Runge–Kutta observer and an adaptive threshold for a class of closed-loop non-linear systems. The purpose of this paper is to improve the rapidity and accuracy of fault detection.

Design/methodology/approach

First, the authors design the H_/H∞ Runge–Kutta fault detection observer, which is used as a residual generator to decouple the residual from the input. The H_ performance index metric in the specified frequency domain is used to describe how sensitive the residual to the fault. The H∞ norm is used to describe the residual robustness to the external disturbance of the systems. The residual generator is designed to achieve the best tradeoff between robustness against unknown disturbances but sensitivity to faults, thus realizing the accurate detection of the fault by suppressing the influence of noise and disturbance on the residual. Next, the design of the H_/H∞ fault detection observer is transformed into a convex optimization problem and solved by linear matrix inequality. Then, a new adaptive threshold is designed to improve the accuracy of fault detection.

Findings

The effectiveness and correctness of the method are tested in simulation experiments.

Originality/value

This paper presents a novel approach to improve the accuracy and rapidity of fault detection for closed-loop non-linear system with disturbances and noise.

Details

Assembly Automation, vol. 40 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 27 July 2012

Anupam Das, J. Maiti and R.N. Banerjee

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with…

1713

Abstract

Purpose

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Design/methodology/approach

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

Findings

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

Research limitations/implications

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

Originality/value

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

Details

International Journal of Quality & Reliability Management, vol. 29 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 6 May 2022

Lijia Cao, Xu Yang, Guoqing Wang, Yang Liu and Yu Hu

The purpose of this paper is to present an actuator fault detection method for unmanned aerial vehicles (UAVs) based on interval observer and extended state observer.

Abstract

Purpose

The purpose of this paper is to present an actuator fault detection method for unmanned aerial vehicles (UAVs) based on interval observer and extended state observer.

Design/methodology/approach

The proposed algorithm has very little model dependency. Therefore, a six-degree-of-freedom linear equation of UAVs is first established, and then, combined with actuator failure and external disturbances in flight control, a steering gear model with actuator failure (such as stuck bias and invalidation) is designed. Meanwhile, an extended state observer is designed for fault detection. Moreover, a fault detection scheme based on interval observer is designed by combining fault and disturbances.

Findings

The method is testified on the extended state observer and the interval observer under the failure of the steering gear and bounded disturbances. The simulation results show that the two types of fault detection schemes designed can successfully detect various types of faults and have high sensitivity.

Originality/value

This research paper studies the failure detection scheme of the UAVs’ actuator. The fault detection scheme in this paper has better performance on actuator faults and bounded disturbances than using regular fault detection schemes.

Details

Aircraft Engineering and Aerospace Technology, vol. 94 no. 10
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 5 April 2023

Khaoula Assadi, Jihane Ben Slimane, Hanene Chalandi and Salah Salhi

This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural…

Abstract

Purpose

This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults.

Design/methodology/approach

The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step.

Findings

The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance.

Originality/value

The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 42 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 16 August 2021

Gulay Unal

The purpose of this study is to present a new integrated structure for a fault tolerant aircraft control system because fault diagnosis of flight control systems is extremely…

Abstract

Purpose

The purpose of this study is to present a new integrated structure for a fault tolerant aircraft control system because fault diagnosis of flight control systems is extremely important in obtaining healthy flight. An approach to detect and isolate aircraft sensor faults is proposed, and a new integrated structure for a fault tolerant aircraft control system is presented.

Design/methodology/approach

As disturbance and sensor faults are mixed together in a flight control system, it is difficult to isolate any fault from the disturbance. This paper proposes a robust unknown input observer for state estimation and fault detection as well as isolation using fuzzy logic.

Findings

The dedicated observer scheme (DOS) and generalized observer scheme (GOS) are used for fault detection and isolation in an observer-based approach. Using the DOS, it has been shown through simulation that sensor fault detection and isolation can be made, but here the threshold value must be well chosen; if not, the faulty sensor cannot be correctly isolated. On the other hand, the GOS is more usable and flexible than the DOS and allows isolation of faults more correctly and for a fuzzy logic-based controller to be used to realize fault isolation completely.

Originality/value

The fuzzy logic approach applied to the flight control system adds an important key for sensor fault isolation because it reduces the effect of false alarms and allows the identification of different kinds of sensor faults. The proposed approach can be used for similar systems.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 10
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 22 February 2024

Subrat Kumar Barik, Smrutimayee Nanda, Padarbinda Samal and Rudranarayan Senapati

This paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.

Abstract

Purpose

This paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.

Design/methodology/approach

It is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.

Findings

DTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.

Originality/value

Verification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 43 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

1 – 10 of over 3000