scholarly journals Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6356
Author(s):  
Salman Khalid ◽  
Woocheol Lim ◽  
Heung Soo Kim ◽  
Yeong Tak Oh ◽  
Byeng D. Youn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Detection System ◽  
Fault Detection And Diagnosis ◽  
Sensor Selection ◽  
Test Case ◽  
Steam Power ◽  
Selection Scheme ◽  
Waterwall Tube ◽  
Detection And Diagnosis

Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

scholarly journals Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Guang Hu ◽  
Taotao Zhou ◽  
Qianfeng Liu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
State Of The Art ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Detection And Diagnosis

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

scholarly journals Model-Based Fault Detection and Diagnosis for Spacecraft with an Application for the SONATE Triple Cube Nano-Satellite

Aerospace ◽  
2019 ◽  
Vol 6 (10) ◽  
pp. 105 ◽  
Author(s):  
Kirill Djebko ◽  
Frank Puppe ◽  
Hakan Kayal
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Fault Detection And Diagnosis ◽  
Generic Model ◽  
Diagnosis System ◽  
Counter Measures ◽  
Model Based ◽  
Human Operators ◽  
Detection And Diagnosis ◽  
Mission Operation

The correct behavior of spacecraft components is the foundation of unhindered mission operation. However, no technical system is free of wear and degradation. A malfunction of one single component might significantly alter the behavior of the whole spacecraft and may even lead to a complete mission failure. Therefore, abnormal component behavior must be detected early in order to be able to perform counter measures. A dedicated fault detection system can be employed, as opposed to classical health monitoring, performed by human operators, to decrease the response time to a malfunction. In this paper, we present a generic model-based diagnosis system, which detects faults by analyzing the spacecraft’s housekeeping data. The observed behavior of the spacecraft components, given by the housekeeping data is compared to their expected behavior, obtained through simulation. Each discrepancy between the observed and the expected behavior of a component generates a so-called symptom. Given the symptoms, the diagnoses are derived by computing sets of components whose malfunction might cause the observed discrepancies. We demonstrate the applicability of the diagnosis system by using modified housekeeping data of the qualification model of an actual spacecraft and outline the advantages and drawbacks of our approach.

The Fault Detection and Diagnosis System of Hydraulic Excavator Based on PXI Interface Technology

2012 ◽  
Vol 268-270 ◽  
pp. 1440-1443
Author(s):  
Liang Mi ◽  
Keng Feng ◽  
Huan Liang Li ◽  
Li Fu Shao
Keyword(s):  
Fault Detection ◽  
Data Storage ◽  
Detection System ◽  
Management Control ◽  
Fault Detection And Diagnosis ◽  
Circuit Board ◽  
Hydraulic Excavator ◽  
Electrical System ◽  
Diagnosis System ◽  
Detection And Diagnosis

The fault detection and diagnosis system, which uses dedicated interface adapter unit and PXI interface modules to perform signal excitation and parameter testing, is introduced in this paper. Its firmware consists of PXI-bus control computer, display control unit, dedicated interface adapter unit, connection cable and power supply unit. The dedicated interface adapter unit (IAU) is the core of hardware platform, and is composed of chassis, I2C-bus data acquisition board, adapter and military aviation sockets. It carries out analog to digital, digital to analog, digital I/O transformation as well as serial communication and CAN-bus communication. The software of the fault detection system is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with repair and diagnosis database. The software provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, repair guidance and data storage. Therefore, this system can implement fault detection of hydraulic excavator on replaceable circuit board and block of the hydraulic system or electrical system. And it can help equipment repairmen and operator perform quick repairs and maintenance to the electrical system and hydraulic circuit of the excavator.

Sign in / Sign up

Export Citation Format

Share Document