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Article

Rosario Miceli, Yasser Gritli, Antonino Di Tommaso, Fiorenzo Filippetti and Claudio Rossi

The purpose of this paper is to present a diagnosis technique, for rotor broken bar in double cage induction motor, based on advanced use of wavelet transform analysis

Abstract

Purpose

The purpose of this paper is to present a diagnosis technique, for rotor broken bar in double cage induction motor, based on advanced use of wavelet transform analysis. The proposed technique is experimentally validated.

Design/methodology/approach

The proposed approach is based on a combined use of frequency sliding and wavelet transform analysis, to isolate the contribution of the rotor fault components issued from vibration signals in a single frequency band.

Findings

The proposed technique is reliable for tracking the rotor fault components over time-frequency domain. The quantitative analysis results based on this technique are the proof of its robustness.

Research limitations/implications

The validity of the proposed diagnosis approach is not limited to the analysis under steady-state operating conditions, but also for time-varying conditions where rotor fault components are spread in a wide frequency range.

Practical implications

The developed approach is best suited for automotive or high power traction systems, in which safe-operating and availability are mandatory.

Originality/value

The paper presents a diagnosis technique for rotor broken bar in double cage induction motor base on advanced use of wavelet transform which allows the extraction of the most relevant rotor fault component issued from axial vibration signal and clamping it in a single frequency bandwidth, avoiding confusions with other components and false interpretations.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

Fatma Yasli and Bersam Bolat

Risk analysis is a critical investigation field for many sectors and organizations to maintain the information management reliable. Since mining is one of the riskiest…

Abstract

Purpose

Risk analysis is a critical investigation field for many sectors and organizations to maintain the information management reliable. Since mining is one of the riskiest sectors for both workers and management, comprehensive risk analysis should be carried out. The purpose of this paper is to explore comprehensively the undesired events that may occur during a particular process with their main reasons and to perform a risk analysis for these events, by developing a risk analysis methodology. For performing risk analysis, discovering and defining the potential accidents and incidents including their root causes are important contributions of the study as distinct from the related literature. The fuzzy approach is used substantially to obtain the important inferences about the hazardous process by identifying the critical risk points in the processes. In the scope of the study, the proposed methodology is applied to an underground chrome mine and obtaining significant findings of mining risky operations is targeted.

Design/methodology/approach

Fault tree analysis and fuzzy approach are used for performing the risk analysis. When determining the probability and the consequences of the events which are essential components for the risk analysis, expressions of the heterogeneous expert group are considered by means of the linguistic terms. Fault tree analysis and fuzzy approach present a quiet convenience solution together to specify the possible accidents and incidents in the particular process and determine the values for the basis risk components.

Findings

This study primarily presents a methodology for a comprehensive risk analysis. By implementing the proposed methodology to the underground loading and conveying processes of a chrome mine, 28 different undesired events that may occur during the processes are specified. By performing risk analysis for these events, it is established that the employee’s physical constraint while working with the shovel in the fore area, the falling of materials on employees from the chute and the scaling bar injuries are the riskiest undesired events in the underground loading and conveying process of the mine.

Practical implications

The proposed methodology provides a confidential and comprehensive method for risk analysis of the undesired events in a particular process. The capability of fault tree analysis for specifying the undesired events systematically and the applicability of fuzzy approach for converting the experts’ linguistic expressions to the mathematical values provide a significant advantage and convenience for the risk analysis.

Originality/value

The major contribution of this paper is to develop a methodology for the risk analysis of a variety of mining accidents and incidents. The proposed methodology can be applied to many production processes to investigate the dangerous operations comprehensively and find out the efficient management strategies. Before performing the risk analysis, determining the all possible accidents and incidents in the particular process using the fault tree analysis provides the effectiveness and the originality of the study. Also, using the fuzzy logic to find out the consequences of the events with experts’ linguistic expressions provides an efficient method for performing risk analysis.

Details

Journal of Enterprise Information Management, vol. 31 no. 4
Type: Research Article
ISSN: 1741-0398

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Article

Babak Vaseghi, Noureddine Takorabet and Farid Meibody‐Tabar

The purpose of this paper is to present a study and analysis of insulation failure inter‐turn fault in induction machines (IMs).

Abstract

Purpose

The purpose of this paper is to present a study and analysis of insulation failure inter‐turn fault in induction machines (IMs).

Design/methodology/approach

A time stepping finite element method (FEM) analysis is performed for the study of IM with inter‐turn fault and determining the machine parameters (self and mutual inductances) after occurring fault. A simple dynamic model for IM with inter‐turn fault is presented. The model parameters are obtained by FEM analysis. An experimental test is also carried out to verify the results.

Findings

The behavior of IM is studied under various insulation failure inter‐turn fault conditions and severity using FEM. The paper's results help the machine designers to improve the fault tolerance as well the overall design of the machine drive system. It can also be useful for predict and detection of fault in IM.

Practical implications

Predicting and detection of turn faults in IM are in industry very helpful because it avoids the fully damage of IM and it is more easy to repair the machine. Designing a fault tolerant IM is required in some applications for increasing the reliability.

Originality/value

By using FEM for studying the fault, the machine parameters which are calculated with FEM and the study's results are very precise and accurate because the flux fluctuation after occurring fault has been taken into account. On the other hand, the fault model is very fast, global and accurate. It can be used in model‐based health monitoring systems.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 29 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

Ravikumar KN, Hemantha Kumar, Kumar GN and Gangadharan KV

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning…

Abstract

Purpose

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Design/methodology/approach

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

Findings

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

Practical implications

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

Originality/value

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Details

Journal of Quality in Maintenance Engineering, vol. 27 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article

Pratesh Jayaswal, S.N. Verma and A.K. Wadhwani

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis…

Abstract

Purpose

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

Design/methodology/approach

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

Findings

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

Practical implications

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

Originality/value

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

Details

Journal of Quality in Maintenance Engineering, vol. 16 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article

Sophi Shilpa Gururajapathy, Hazlie Mokhlis and Hazlee Azil Illias

The purpose of this paper is to identify faults in distribution systems which are unavoidable because of adverse weather conditions and unexpected accidents. Hence, quick…

Abstract

Purpose

The purpose of this paper is to identify faults in distribution systems which are unavoidable because of adverse weather conditions and unexpected accidents. Hence, quick fault location is vital for continuous power supply. However, most fault location methods depend on the stored database for locating fault. The database is created by simulation, which is time consuming. Therefore, in this work, a comprehensive fault location method to detect faulty section and fault distance from one-ended bus using limited simulated data is proposed.

Design/methodology/approach

The work uses voltage sag data measured at a primary substation. Support vector machine estimates the data which are not simulated. The possible faulty section is determined using matching approach and fault distance using mathematical analysis.

Findings

This work proposed a ranking analysis for multiple possible faulty sections, and the fault distance is calculated using Euclidean distance approach.

Practical implications

The research work uses Malaysian distribution system as it represents a practical distribution system with multiple branches and limited measurement at primary substation. The work requires only metering devices to identify fault which is cost effective. In addition, the distribution system is simulated using real-time PSCAD by which the capability of proposed method can be fully tested.

Originality/value

The paper presents a new method for fault analysis. It reduces simulation time and storage space of database. The work identifies faulty section and ranks the prior faulty section. It also identifies fault distance using a mathematical approach.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 38 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

Paulo Cezar Monteiro Lamim Filho, Jorge Nei Brito, Vinicius Augusto Diniz Silva and Robson Pederiva

The objective is the application of vibration analysis for the detection and diagnosis of low isolation between the stator coil wind and the voltage phase unbalance in…

Abstract

Purpose

The objective is the application of vibration analysis for the detection and diagnosis of low isolation between the stator coil wind and the voltage phase unbalance in induction motors with different numbers of poles. The purpose of this paper is to provide an approach for maintenance engineers for diagnosis electrical fault through the vibration analyses.

Design/methodology/approach

A detailed review of previous work carried out by some researchers and maintenance engineers in the area of machine fault detection is performed. By vibration analysis, the spectra were collected, which used to analyze the failure. Vibration spectra could detect particular characteristic for each fault in an initial condition, so the machine health can be preserved.

Findings

Results show the efficiency of the technique of vibration analysis and their relevance to detect and diagnose faults in different induction motors. In this way, it may be included in future predictive maintenance programs.

Practical implications

The paper presents a laboratory investigation carried out through an experimental set-up for the study of fault, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance.

Originality/value

The main contribution of the paper has been the characterization of one more tool that makes the predictive maintenance process more efficient, effective and faster, increasing the reliability and availability of equipment.

Details

Journal of Quality in Maintenance Engineering, vol. 19 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

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Article

Yinhua Liu, Rui Sun and Sun Jin

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality…

Abstract

Purpose

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

Design/methodology/approach

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

Findings

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

Originality/value

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article

Rongxing Duan, Shujuan Huang and Jiejun He

This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop…

Abstract

Purpose

This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail.

Design/methodology/approach

First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency.

Findings

In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis.

Originality/value

The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

Details

Engineering Computations, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-4401

Keywords

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Article

Bratislav Tasic, Jos J. Dohmen, E. Jan W. ter Maten, Theo G.J. Beelen, Wil H.A. Schilders, Alex de Vries and Maikel van Beurden

Imperfections in manufacturing processes may cause unwanted connections (faults) that are added to the nominal, “golden”, design of an electronic circuit. By fault

Abstract

Purpose

Imperfections in manufacturing processes may cause unwanted connections (faults) that are added to the nominal, “golden”, design of an electronic circuit. By fault simulation one simulates all situations. Normally this leads to a large list of simulations in which for each defect a steady-state (direct current (DC)) solution is determined followed by a transient simulation. The purpose of this paper is to improve the robustness and the efficiency of these simulations.

Design/methodology/approach

Determining the DC solution can be very hard. For this the authors present an adaptive time-domain source stepping procedure that can deal with controlled sources. The method can easily be combined with existing pseudo-transient procedures. The method is robust and efficient. In the subsequent transient simulation the solution of a fault is compared to a golden, fault-free, solution. A strategy is developed to efficiently simulate the faulty solutions until their moment of detection.

Findings

The paper fully exploits the hierarchical structure of the circuit in the simulation process to bypass parts of the circuit that appear to be unaffected by the fault. Accurate prediction and efficient solution procedures lead to fast fault simulation.

Originality/value

The fast fault simulation helps to store a database with detectable deviations for each fault. If such a detectable output “matches” a result of a product that has been returned because of malfunctioning it helps to identify the subcircuit that may contain the real fault. One aims to detect as much as possible candidate faults. Because of the many options the simulations must be very efficient.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33 no. 4
Type: Research Article
ISSN: 0332-1649

Keywords

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