Research on application of equipment fault diagnosis technology based on FTA

Hu, Yamin; Fang, Qiqing; Zhang, Wei; Ya-hui, HU

doi:10.1145/3501409.3501467

Cited by 3 publications

(3 citation statements)

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“…The brushless DC motor control drive circuit adopts the IPM power drive module, as shown in Figure 2, which has a short circuit, an overvoltage, and overcurrent protection. It can meet the requirements of airborne radar for volume, weight, and high reliability [3]. In the PWM controller, the current detection signal contains AC components, and a low-pass current filter link should be added.…”

Section: Design Of Pwm Controller For Brushless DC Motormentioning

confidence: 99%

Design of Airborne Radar Servo Control System

Wang

2023

J. Phys.: Conf. Ser.

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The brushless DC motor is driven by a square wave. Compared with the sinusoidal synchronous motor, the control is simple. The airborne radar servo control system described in this paper uses a brushless DC motor drive, PWM control, and angular position. The speed detection feedback loop is composed of a brushless resolver and a resolver for digital converters. The control system adopts the current, speed, and position closed-loop design. The design simulation is carried out by using MATLAB/Simulink to guide the control system design. The system design uses PID incremental control and an integral separation control algorithm. Through the rational design of software and hardware, the airborne radar servo system has achieved high dynamic and static performance, which meets the performance requirements of the airborne radar system.

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Section: Design Of Pwm Controller For Brushless DC Motormentioning

confidence: 99%

Design of Airborne Radar Servo Control System

Wang

2023

J. Phys.: Conf. Ser.

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“…For example, Wang et al [18] utilize the LVQ neural network to accurately classify and identify the power load characteristics of substation, which proves the effectiveness of this method. Hu et al [19] apply the LVQ neural network to fault prediction of electrical equipment in substations, and the results verify the advantages of the algorithm in terms of prediction accuracy and computation rate [20,21]. On the other hand, the LVQ neural network has shown promise in assessing system performance, but its application to substation relay protection systems is still not widespread [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al. [19] apply the LVQ neural network to fault prediction of electrical equipment in substations, and the results verify the advantages of the algorithm in terms of prediction accuracy and computation rate [20, 21]. On the other hand, the LVQ neural network has shown promise in assessing system performance, but its application to substation relay protection systems is still not widespread [22‐24].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach

Wang,

Zhang,

Wang

et al. 2024

The Journal of Engineering

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This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems.

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Fault Tree Analysis for Reliability Analysis of Wind Turbines Considering the Imperfect Repair Effect

Ali,

Rana,

Niaz

et al. 2023

ejtas

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Wind turbines are complex and expensive equipment, requiring high reliability and low maintenance costs. However, most of the existing fault tree analysis (FTA) methods for reliability analysis of wind turbines assume that the repair of wind turbines can restore them to as good as new condition, which is called perfect repair. This assumption may not be realistic in practice, as the repair may not fully recover the original performance or functionality of the equipment or may introduce new defects or errors. This phenomenon is called imperfect repair, which can reduce the reliability of wind turbines over time. To consider the imperfect repair effect in reliability analysis, we present a new FTA approach in this study. In order to predict and assess the failure intensity and dependability of wind turbines under imperfect repair, the proposed FTA technique uses a log-linear proportional intensity model (LPIM). Failure probability, failure rate, and mean time to failure can all be improved with the suggested FTA technique for wind turbines operating with poor repair. The proposed FTA method can also identify the critical components or failure modes most affected by the imperfect repair effect and suggest preventive maintenance actions to improve the reliability of wind turbines. We demonstrate the applicability and effectiveness of the proposed FTA method through a case study on a real or hypothetical wind turbine system under imperfect repair. The findings indicate that the proposed FTA method offers a more precise and authentic assessment of the reliability of wind turbines in the presence of imperfect repair, in contrast to existing FTA methods that assume perfect repair. The findings also demonstrate that the electrical system, hydraulic system, gearbox, generator, and blade are the most critical components or failure modes affecting the system's reliability.

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Research on application of equipment fault diagnosis technology based on FTA

Cited by 3 publications

References 0 publications

Design of Airborne Radar Servo Control System

Design of Airborne Radar Servo Control System

A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach

Fault Tree Analysis for Reliability Analysis of Wind Turbines Considering the Imperfect Repair Effect

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