Baseline-free sensor fault detection for piezoelectric array

Author(s):  
Qibo Mao ◽  
Yuande Wang ◽  
Shizuo Huang

In this study, a new methodology is presented to detect the sensor fault for piezoelectric array based on the filtered frequency response function (FRF) shapes. The proposed method does not require prior knowledge about healthy piezoelectric array. First, the imaginary parts of FRFs from the piezoelectric array during vibration are measured and normalized to obtain the FRF shapes in different frequencies. Then the irregularities in these FRF shapes are extracted by using high-pass filter with properly chosen cut-off frequency. These abnormal irregularities on the filtered FRF shape curves indicate the location of the faulty sensor, due to the irregularity of FRF shapes introduced by the faulty piezoelectric element. The proposed sensor fault method is experimentally demonstrated on a clamped-clamped steel beam mounted with piezoelectric buzzer array. Two common piezoelectric sensor fault types including sensor breakage and debonding are evaluated. The experimental results indicate that the proposed method has great potential in the detection of the sensor fault for piezoelectric array as it is simple and does not require the FRF data of the healthy sensor array as a baseline.

2017 ◽  
Vol 29 (17) ◽  
pp. 3436-3443 ◽  
Author(s):  
Hwee Kwon Jung ◽  
Gyuhae Park

This article presents a technique for detecting structural impact and damage by integrating passive and active-sensing approaches. An L-shaped piezoelectric sensor array was used to detect and localize impacts by measuring the response of structures. It was found that since this method does not require prior knowledge of structures such as the direction-dependent wave velocity profiles, accurate results could be achieved even on anisotropic structures. This sensor array was then extended to include an active-sensing approach, and the same sensor array was used for damage detection by measuring scattering and reflected waves. A series of experiments was carried out to demonstrate the proposed techniques. The superior capability of the proposed techniques was experimentally demonstrated.


Author(s):  
Maryam Abata ◽  
Mahmoud Mehdi ◽  
Said Mazer ◽  
Moulhime El Bekkali ◽  
Catherine Algani

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada

Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.


2020 ◽  
Vol 53 (2) ◽  
pp. 86-91
Author(s):  
Benjamin Jahn ◽  
Michael Brückner ◽  
Stanislav Gerber ◽  
Yuri A.W. Shardt

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1543 ◽  
Author(s):  
Fernando Garramiola ◽  
Jon del Olmo ◽  
Javier Poza ◽  
Patxi Madina ◽  
Gaizka Almandoz

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