Fault Detection Rules Research Articles

Enhancing Incipient Fault Detection for Interface Converter Sensors through Signal Correlation Analysis

Incipient faults in interface converters can potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves system reliability. In this paper, a detection method in correlation space is proposed to address this problem, which is based on the inherent feature of correlation changes when a fault occurs. First, a convolution process is used to weaken noise and highlight the correlation feature. Second, the current signals are transmitted to correlation space by using the Pearson correlation coefficient. Third, an accumulation and a reference compensation method are designed for enhancing the features and equalizing influence of reference adjustment. Finally, a fault detection rule is designed based on correlation features and fault excitation. Experiments on a hardware‐in‐loop(HIL) semi‐physical platform indicate that the proposed method outperforms three commonly used correlation analysis algorithms. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Dynamic PMSM Motor Model with Turn Short Fault for Wind Power Generation

A PMSM (Permanent Magnet Synchronous Motor) stator inter turn short fault generates the back emf and impedance unbalance element. Those unbalance element cause the torque ripple and the escalating partial heat defect. This paper proposes a dynamic motor model under the internal turn short fault. The proposed model is derived in the positive and negative sequence synchronous reference frame to be easily utilized for the fault detection rule. To validate the proposed scheme, the finite elements method (FEM) simulation with electric circuit is conducted on the PMSM.

Wavelet based rule for fault detection

The paper presents wavelet based fault detection method and the analysis of error rates. The proposed method is based on signal representations in Daubechies wavelet bases. For the designed fault detection rule, the upper bound of non-detection probability is derived. The proposed detection method was tested on benchmark data from the Lublin Sugar Factory in Poland.

Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

This is the second part of a study on diagnostic Bayesian networks (DBNs)-based method for diagnosing faults in air handling units (AHUs) in buildings. In this part, 4 DBNs are developed to diagnose faults in heating/cooling coils, sensors and faults in secondary supply chilled water/heating water systems. There are 18 typical faults concerned and 35 fault detectors introduced. The DBNs are developed mainly on the basis of first principles and fault patterns resulted from literature and three AHU fault detection and diagnosis (FDD) projects. Efficient fault detection rules/methods from a comprehensive literature survey are integrated into the DBNs. Also, some new fault detection rules are developed. The 4 DBNs were evaluated using experimental data from ASHRAE Project RP-1312. Results show that the proposed DBNs effectively diagnosed AHU faults.

Fault Diagnosis of Autonomous Underwater Vehicles

In this study, we propose the least disturbance algorithm adding scale factor and shift factor. The dynamic learning ratio can be calculated to minimize the scale factor and shift factor of wavelet function and the variation of net weights and the algorithm improve the stability and the convergence of wavelet neural network. It was applied to build the dynamical model of autonomous underwater vehicles and the residuals are generated by comparing the outputs of the dynamical model with the real state values in the condition of thruster fault. Fault detection rules are distilled by residual analysis to execute thruster fault diagnosis. The results of simulation prove the effectiveness.

ROBUST FAULT DETECTION AND HANDLING IN CONTROL OF UNCERTAIN TRANSPORT-REACTION PROCESSES

This paper presents an integrated robust fault detection and fault-tolerant control architecture for transport-reaction processes modeled by quasi-linear parabolic partial differential equations with uncertain variables, control constraints and control actuator faults. Using an appropriate reduced-order model that captures the dominant process dynamics, the proposed architecture comprises a family of robustly stabilizing bounded feedback controllers with explicitly characterized stability and uncertainty attenuation properties, a performance- based fault detection scheme and a high-level supervisor that reconfigures the control actuators upon fault detection in a way that maintains robust closed-loop stability. The key idea is to shape the closed-loop performance via robust control in a way that facilitates the design of robust fault detection rules that are less sensitive to the adverse effects of uncertainty. The results are demonstrated using a non-isothermal tubular reactor example with recycle.

A Case Study in Industrial Bioprocess Advisory System Development

This paper describes the background and motivation for the construction of a fault detection and advisory system for an industrial fermentation process plant. The need to utilise both algorithmic and rule based fault detection methods is discussed. Following this, the implementation strategy based on the use of G2 from Gensym is outlined. The KAT knowledge elicitation method was used to efficiently capture the fault detection rule set. Examples of the method of elicitation, form of the rules and ease of implementation are given. Finally, the integration with multivariate data-based methods for fault detection and process application of the combined system is described.