Self-tuning fault diagnosis of MEMS

Izadian, Afshin

doi:10.1016/j.mechatronics.2013.08.006

Cited by 17 publications

(11 citation statements)

References 15 publications

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“…The linear OCV-SOC relationship is given by, (10) where is the slope of the line and d is the no-charge OCV. Using this linear relationship results in a linear li-ion battery model where the terminal voltage of (3) can now be given by, (11) Comparing ( …”

Section: Linear Modelmentioning

confidence: 99%

“…Accurate estimation of system and measurement noise variances has proven to profoundly impact on the residual generation process. Noise over-or under-estimation can result in loss of information during residual generation process, and hence affect the fault diagnosis performance [11]. The fault diagnosis performance as a function of battery model and the noise estimation is as shown in the surface plot of Figure 10.…”

Section: Non-linear Battery Modelmentioning

confidence: 99%

“…MMAE is a robust fault detection and identification technique with extensive applications in the aerospace industry [9,10] and recently in the fault detection of micro electro mechanical systems [11,12]. With the application of Kalman filters and extended Kalman filters under multiple model adaptive estimation technique [12,13,14], the performance deterioration can be minimized to a very large extent.…”

Section: Introductionmentioning

confidence: 99%

“…With the application of Kalman filters and extended Kalman filters under multiple model adaptive estimation technique [12,13,14], the performance deterioration can be minimized to a very large extent. While the use of observer based fault diagnosis through MMAE offers good li-ion battery diagnosis performance [7], for it to be sensitive to minute battery performance shift, as in the case of condition monitoring, it is imperative that the li-ion battery model offers good fault detection and diagnosis characteristics and the system noise is correctly estimated [11]. By avoiding noise over or under estimation and appropriate model selection the quality of residuals can be enhanced.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model-Based Adaptive Fault Diagnosis in Lithium Ion Batteries: A Comparison of Linear and Nonlinear Approaches

Sidhu

Izadian

Anwar

2017

SAE Technical Paper Series

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In this paper, multiple-model adaptive estimation techniques have been successfully applied to fault detection and identification in lithium-ion batteries. The diagnostic performance of a battery depends greatly on the modeling technique used in representing the system and the associated faults under investigation. Here, both linear and non-linear battery modeling techniques are evaluated and the effects of battery model and noise estimation on the over-charge and over-discharge fault diagnosis performance are studied. Based on the experimental data obtained under the same fault scenarios for a single cell, the non-linear model based detection method is found to perform much better in accurately detecting the faults in real time when compared to those using linear model based method.

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Section: Linear Modelmentioning

confidence: 99%

Section: Non-linear Battery Modelmentioning

confidence: 99%