Identification Of Stochastic Multivariable Continuous-Time Systems

Zhang, H.Y.; Chen, J.

doi:10.1080/02286203.1990.11760097

Cited by 2 publications

(1 citation statement)

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“…This is the main reason because bearings are the object of permanent maintenance programs in most industries and several works are devoted to propose methods to monitor the bearing health condition. For instance in [1], the authors compute a mathematical model of the DC motor based on the block-pulse function series method [2] and then a multilayer neural network configuration to detect changes on the model parameters. Neural networks combined with other techniques such as fuzzy logic have also been used for fault diagnosis of DC motors [3].…”

Section: Introductionmentioning

confidence: 99%

Fault detection on bearings coupled to permanent magnet DC motors by using a generalized Takagi-Sugeno PI observer

Martinez-Garcia

Osorio-Gordillo

Astorga‐Zaragoza

et al. 2016

2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol)

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Abstract-This paper presents a fault detection system for rotative machinery. A permanent-magnet DC motor is used as case of study. The main idea is to estimate on-line the non-load torque (T0) in order to monitor the bearing health condition. The fault detection system is based on the design of a generalized Takagi-Sugeno PI (proportional-integral) observer. The main advantage of this approach is that it can be easily implemented because the observer gains are obtained by solving a set of LMIs (linear matrix inequalities). Moreover, the method can be extended to more complicated nonlinear systems by using the Takagi-Sugeno approach. A simulation is performed to show that this fault detection scheme can be applied to detect abrupt faults on rotative machinery which can lead the system to undesirable performance caused by vibrations or breakdown.

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Section: Introductionmentioning

confidence: 99%