Dynamic Information Transfer in Vibration Signal Applied to Early Fault Detection of Hydropower Generation Unit

基于振動信號動態信息傳遞的水電機組早起故障檢測

10.6937/TWC.202406_72(2).0001

SHENGMIN REN(任晟民)；PENGFEI WANG(王鵬飛)；KUN WANG(王坤)；DIYI CHEN(陳帝伊)；BIN WANG(王斌)

Early fault detection ； Hydropower generation units ； Signal-processing ； Information transfer ； System safety ； 早期故障檢測 ； 水力發電機組 ； 信號處理 ； 信息傳遞 ； 系統安全性

台灣水利

72卷2期（2024 / 06 / 01）

1 - 20

英文;繁體中文

Early fault detection of hydropower generation unit (HGU) is of great significance for the safe operation of the hydropower plant. Most of the related research focuses on the decomposition and feature extraction of single vibration monitoring signals. However, HGU is a typical coupling system with multi-channel vibration signals, and the subtle information transfer among signals is the precursor factor leading to the changes in the whole system. There has not been any research considering this potential factor in HGU or other systems. An improved principal component analysis monitoring model based on dynamic information transmission (DIT-PCA) is proposed. The process state of the unit is monitored by PCA (Principal components analysis) of the subtle dynamic transmitted information between the unit monitoring variables, which information is revealed for the first time. Normal monitoring samples are used for DIT-PCA offline training at first. Then, the confidence limits and fault contribution rates of two monitoring indicators Hotelling's T 2 statistics and SPE statistics of the model after training are applied to monitor the same test samples to achieve online fault detection and location. Compared with PCA and KICA, the success rate of T 2 statistics in DIT-PCA is 42.756%, which is much higher than that of PCA and KICA methods. The success rate of SPE statistics is 94.2%, and DIT-PCA has higher sensitivity. Moreover, the proposed model is applied to the state process of a real HGU, which has a superior sensitivity than two available detection methods. The results provide a direct reference for the early fault detection of the engineering system.

水電機組（HGU）的早期故障檢測對水電廠的安全運行具有重要意義。相關研究大多集中于單壹振動監測信號的分解與特征提取。然而，HGU是壹個典型的多通道振動信號藕合系統，信號間的細微信息傳遞是導致整個系統變化的前兆因素。在HGU或其他系統中還沒有考慮到這壹潛在因素的研究。本文提出了壹種基于動態信息傳遞的改進型主成分分析監測模型（DIT-PCA）。通過PCA（Principal components analysis）對機組監測變量間的細微動態傳遞信息進行監測，首次揭示了機組的過程狀態信息。首先利用正常監測樣本進行DIT-PCA離線訓練。然後，將訓練後模型的兩個監測指標霍特林的T^2統計量和SPE統計量的置信限和故障貢獻率應用于監測相同的測試樣本，實現在線故障檢測和定位。此外，將所提出的模型應用于實際HGU的狀態過程，與PCA和KICA相比，DIT-PCA中T^2統計量的成功率42.756%，遠高於PCA和KICA方法的成功率，SPE統計量的成功率94.2%，DIT-PCA具有更高的靈敏度。研究結果工程系統的早期故障檢測提供了直接參考。