Quispe-Astorga Amilcar, Coaquira-Castillo Roger Jesus, Utrilla Mego L Walter, Herrera-Levano Julio Cesar, Concha-Ramos Yesenia, Sacoto-Cabrera Erwin J, Moreno-Cardenas Edison
LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru.
TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru.
Sensors (Basel). 2025 Jun 11;25(12):3647. doi: 10.3390/s25123647.
Precision air conditioning (PAC) systems are prone to various types of failures, leading to inefficiencies, increased energy consumption, and possible reductions in equipment performance. This study proposes an automatic real-time fault detection and diagnosis system. It classifies events as either faulty or normal by analyzing key status signals such as pressure, temperature, current, and voltage. This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. The work was carried out on a Rittal PAC, model SK3328.500 (cooling unit), installing capacitive pressure sensors, Hall effect current sensors, electromagnetic induction voltage sensors, infrared temperature sensors, and thermocouple-type sensors. For the implementation of the system, a dataset of PAC status signals was obtained, initially consisting of 31,057 samples after a preprocessing step using the Random Under-Sampler (RUS) module. A database with 20,000 samples was obtained, which includes normal and failed operating events generated in the PAC. The selection of the models is based on accuracy criteria, evaluated by testing in both offline (database) and real-time conditions. The Support Vector Machine (SVM) model achieved 93%, Decision Tree (DT) 93%, Gradient Boosting (GB) 91%, K-Nearest Neighbors (KNN) 83%, and Naive Bayes (NB) 77%, while the Random Forest (RF) model stood out, having an accuracy of 96% in deferred tests and 95.28% in real-time. Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%.
精密空调（PAC）系统容易出现各种类型的故障，从而导致效率低下、能耗增加以及设备性能可能下降。本研究提出了一种自动实时故障检测与诊断系统。它通过分析压力、温度、电流和电压等关键状态信号，将事件分类为故障或正常。本研究基于数据驱动模型和机器学习，针对五种类型的系统故障提出了一种特定策略。该工作是在威图PAC（型号SK3328.500，制冷单元）上进行的，安装了电容式压力传感器、霍尔效应电流传感器、电磁感应电压传感器、红外温度传感器和热电偶型传感器。为了实现该系统，获取了一个PAC状态信号数据集，在使用随机欠采样器（RUS）模块进行预处理步骤后，最初包含31,057个样本。获得了一个包含20,000个样本的数据库，其中包括PAC中生成的正常和故障运行事件。模型的选择基于准确性标准，通过在离线（数据库）和实时条件下进行测试来评估。支持向量机（SVM）模型的准确率为93%，决策树（DT）为93%，梯度提升（GB）为91%，K近邻（KNN）为83%，朴素贝叶斯（NB）为77%，而随机森林（RF）模型表现突出，在延迟测试中的准确率为96%，在实时测试中的准确率为95.28%。最后，使用最佳选择的模型进行了实时验证测试，模拟了PAC系统的真实环境，准确率达到了93.49%。
