Fuzzy fault isolation using gradient information and quality criteria from system identification models

Serdio, Francisco; Lughofer, Edwin; Pichler, Kurt; Pichler, Markus; Buchegger, Thomas; Efendic, Hajrudin

doi:10.1016/j.ins.2015.04.008

Cited by 33 publications

(15 citation statements)

References 26 publications

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“…The first-class includes the detection of single faults by analyzing one or multiple parameters; the second class covers the detection of different faults with multiple parameters and processing techniques, and the last one contains the mixed techniques of various computing-intensive approaches to analyze different electrical and mechanical parameters in order to detect multiple faults [61][62][63][64]. In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts are made for the application of intelligent algorithms [66,67] including new approaches to fault detection and isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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show abstract

“…Fault detection and isolation (FDI) has been as well active fields of research for the standards of higher safety and reliability (see other related works 8,[15][16][17][18][19][20][21]. However, in practical engineering, as a result of unexpected factors, such as time delays, model uncertainties, disturbances, and noises, may occur in the faulty systems, it is quite difficult to get the accurate size and shape of the fault from only an FDI scheme.…”

Section: Introductionmentioning

confidence: 99%

Adaptive estimation and output feedback FTC for nonlinear systems with unknown nonlinearities and faults

Huang

Zhang

Guo

et al. 2018

Intl J Robust & Nonlinear

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Summary The adaptive estimation mechanism and output feedback fault‐tolerant control (FTC) design is concerned for nonlinear systems with faults, where the unknown nonlinearities are not required to satisfy the Lipschitz condition. An intermediate variable–based adaptive output feedback FTC with an adaptive parameter estimator and an adaptive state tracker is designed. The designed controller can be updated online to compensate the effect of nonlinearities and process faults on systems so that the states of the closed‐loop transformed system are uniformly bounded. The control output is able to be adjusted online by the designed event‐triggering mechanism to maintain fluctuating within a prescribed small range. Numerical examples exhibit the simulation results under the proposed FTC.

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“…Fault detection determines whether possible faults have occurred; early detection helps operators and maintenance personnel take corrective actions to prevent developing abnormal events from leading to serious process upsets. Fault isolation (also called fault identification) locates faulty variables closely related to detected faults; the isolation results assist field experts in diagnosing the faults precisely, i.e., it helps the system operators to determine which parts should be repaired or replaced [3]. Fault diagnosis investigates the root causes and/or sources of occurring faults.…”

Section: Introductionmentioning

confidence: 99%

A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

Jang²,

Yoo³

et al. 2018

Energies

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Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space; the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.

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Fuzzy fault isolation using gradient information and quality criteria from system identification models

Cited by 33 publications

References 26 publications

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Adaptive estimation and output feedback FTC for nonlinear systems with unknown nonlinearities and faults

A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

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