Diagnosis of an incipient sensor fault in a galvanising plant

1999 ◽  
Author(s):  
N. Pezzin ◽  
F. Hamelin ◽  
D. Theilliol ◽  
D. Sauter
Keyword(s):  
Sensor Fault

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection

Baseline-free sensor fault detection for piezoelectric array

2021 ◽  
pp. 1045389X2110188
Author(s):  
Qibo Mao ◽  
Yuande Wang ◽  
Shizuo Huang
Keyword(s):  
Prior Knowledge ◽  
Frequency Response Function ◽  
Sensor Array ◽  
Piezoelectric Element ◽  
Piezoelectric Sensor ◽  
Steel Beam ◽  
Sensor Fault ◽  
Pass Filter ◽  
Sensor Fault Detection ◽  
High Pass

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.

scholarly journals Sensor Fault Diagnosis Based on Adaptive Arc Fuzzy DBN-Petri Net

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 20305-20317
Author(s):  
Shenglei Zhao ◽  
Jiming Li ◽  
Xuezhen Cheng
Keyword(s):  
Fault Diagnosis ◽  
Petri Net ◽  
Sensor Fault

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

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.

scholarly journals Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2248
Author(s):  
Dimitrios A. Papathanasopoulos ◽  
Konstantinos N. Giannousakis ◽  
Evangelos S. Dermatas ◽  
Epaminondas D. Mitronikas
Keyword(s):  
Fault Diagnosis ◽  
Harmonic Component ◽  
Motor Drives ◽  
Brushless Dc Motor ◽  
Position Sensor ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Brushless Dc ◽  
Non Invasive ◽  
Dc Motor Drives

A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.

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