中文标题#
基於人工智慧的工業泵故障診斷機器學習方法
英文标题#
AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
中文摘要#
本研究提出了一種實用的方法，利用來自大型垂直離心泵在嚴苛海洋環境中的實際傳感器數據，實現工業泵系統的早期故障檢測。 監測了五個關鍵運行參數：振動、溫度、流量、壓力和電流。 應用了雙閾值標註方法，結合固定的工程限值和自適應閾值，自適應閾值計算為歷史傳感器值的第 95 百分位數。 為了解決已記錄故障的稀有性，使用領域特定規則將合成故障信號注入數據中，模擬合理操作範圍內的關鍵警報。 訓練了三種機器學習分類器 —— 隨機森林、極端梯度提升（XGBoost）和支持向量機（SVM），以區分正常運行、早期警告和關鍵警報。 結果表明，隨機森林和 XGBoost 模型在所有類別中均表現出高準確性，包括代表罕見或新興故障的少數情況，而 SVM 模型對異常的敏感性較低。 視覺分析，包括分組的混淆矩陣和時間序列圖，表明所提出的混合方法具有強大的檢測能力。 該框架可擴展、可解釋，並適用於實時工業部署，在故障發生前支持主動維護決策。 此外，它可以適應具有類似傳感器架構的其他機械，突顯了其作為複雜系統預測性維護可擴展解決方案的潛力。
英文摘要#
This study presents a practical approach for early fault detection in industrial pump systems using real-world sensor data from a large-scale vertical centrifugal pump operating in a demanding marine environment. Five key operational parameters were monitored: vibration, temperature, flow rate, pressure, and electrical current. A dual-threshold labeling method was applied, combining fixed engineering limits with adaptive thresholds calculated as the 95th percentile of historical sensor values. To address the rarity of documented failures, synthetic fault signals were injected into the data using domain-specific rules, simulating critical alerts within plausible operating ranges. Three machine learning classifiers - Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) - were trained to distinguish between normal operation, early warnings, and critical alerts. Results showed that Random Forest and XGBoost models achieved high accuracy across all classes, including minority cases representing rare or emerging faults, while the SVM model exhibited lower sensitivity to anomalies. Visual analyses, including grouped confusion matrices and time-series plots, indicated that the proposed hybrid method provides robust detection capabilities. The framework is scalable, interpretable, and suitable for real-time industrial deployment, supporting proactive maintenance decisions before failures occur. Furthermore, it can be adapted to other machinery with similar sensor architectures, highlighting its potential as a scalable solution for predictive maintenance in complex systems.
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