Model-based fault detection for a brushless DC motor using parameter estimation

Moseler, Olaf; Isermann, Rolf

doi:10.1109/iecon.1998.724017

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Several more general reviews describing process monitoring methods based on the observer-based method are available [86,135,140]. Several papers have been published using these methods, especially in recent years [136,137,141,162,182,220,233,367].…”

Section: Observer-based Methodsmentioning

confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,133

811

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An alternative group of methods has emerged which do not require the use of an explicit model. This is the key basic construct for the data-driven paradigm. Model-free and non-parametric methods for fault detection, process optimisation and control design are currently at a particularly exciting stage of development.This new advanced textbook by Chiang, Russell and Braatz primarily tackles the data-driven routes to Fault Detection and Diagnosis. It is an outgrowth of a prior Advances in Industrial Control monograph; Russell, Chiang and Braatz. Data-driven Techniques for Fault Detection and Diagnosis in ChemicalProcesses, 2000, ISBN 1-85233-258-1. The new textbook expands the material of the monograph and gives a fuller presentation of some of the alternative modelbased methods, the analytical methods, and of the knowledge-based techniques. vi Series Editors' ForewordThis allows the reader to compare and contrast the different approaches to the problem of fault detection and diagnosis. Thus the text is suitable for advanced courses for process, chemical and control engineers.

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Section: Observer-based Methodsmentioning

confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,133

811

View full text Add to dashboard Cite

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“…Methods based on the parameter estimation: When a fault happens in a BLDC motor, some parameters change. For example, damaged or broken bearings may augment the friction, or an increasing temperature in the stator may increase the phase resistance of all coils [32,33]. These parameter changes can be estimated by different algorithms, among them being the means of the state observers designed from mathematical models that involve the parameters of the BLDC motor.…”

Section: Methods Based On Residual Generationmentioning

confidence: 99%

Review of Methods for Diagnosing Faults in the Stators of BLDC Motors

2022

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A brushless direct current (BLDC) motor is a type of permanent magnet machine that is highly efficient and powerful and requires occasional maintenance. Thanks to these fortunate characteristics, this type of motor has various applications in high-tech industries. However, since BLDC motors are often required to operate at high-speed rotations and under extreme conditions, temperature overshoots can appear during operation, provoking damage to the windings. The purpose of this review is to present the results of a recent investigation and recollection of different methods used for the diagnosis of electrical faults in the stator, such as turn-to-turn short circuits, coil-to-coil short circuits, phase-to-phase short circuits and phase open circuits. In particular, this review presents an analysis of the available diagnosis methods according to the type of fault, the method or technique used for the diagnosis, the evaluated physical variables and the context in which the methods were evaluated (in simulations or in experimental tests). Based on this analysis, the following classifications of diagnostic methods are proposed: signal-based, model-based and data-based methods. Then, the pros and cons of each method class are described and discussed.

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“…The PMAC motor is rotated by the electromagnetic absorption of permanent magnets on the rotor and is controlled by adjusting the input voltage of the stator coils. For that purpose, the PWM duty ratio of the input voltage is modulated by a PWM inverter [3].…”

Section: Modelling Of the Pmac Motormentioning

confidence: 99%

“…identifying the fault and estimating its magnitude (type and severity of fault). A fault detection scheme for the brushless direct current (BLDC) motor used in the MDPS system was studied using the parameter estimation technique [3,4]. A fault detection method for a motor-driven steering column system was proposed by parameter estimation using the Gauss-Newton method [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of model-based sensor fault diagnosis with application to a motor-driven power steering system

Lee

2011

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a theoretical analysis of model-based fault detection and isolation (FDI) and its application to a motor-driven power steering (MDPS) system. In particular, this paper proposes a systematic approach to determining fault detectability and isolability of a linear time-invariant system based on a parity equation. The proposed approach is applied to the MDPS system in order to examine the fault detectability and isolability of the MDPS depending on the number and type of sensors and disturbances. Fault detection and isolation of the phase current sensors and the angular velocity sensor are conducted using a parity equation derived from the mathematical model of the permanent magnet alternating current (PMAC) motor. The fault of the steering torque sensor is detected and isolated by cross-checking with two different torque estimates. An adaptive threshold is proposed to improve the performance and robustness of the proposed FDI method against time-varying model uncertainties. Simulation and vehicle test results show the validity and performance of the proposed FDI method for the MDPS system.

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Model-based fault detection for a brushless DC motor using parameter estimation

Cited by 11 publications

References 13 publications

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems

Review of Methods for Diagnosing Faults in the Stators of BLDC Motors

Analysis of model-based sensor fault diagnosis with application to a motor-driven power steering system

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