In recent times, advanced inspection technique like infrared thermography (IRT) has been used widely for fault diagnosis of electrical equipment in non-contact, non-destructive and non-invasive manner. Manual classification of faults from the IRT images requires more time and effort. In this work, an intelligent scheme for predictive fault diagnosis in VRLA battery is presented for scheduling its preventive maintenance. IR images of pristine and aged VRLA battery in uninterrupted power supply application are acquired using IR camera at different discharging cycles. Image processing of IR images is performed for detection of faults. In order to intelligently classify the faults a fuzzy inference system is developed. Proposed scheme for automatic diagnosis and classification of faults in VRLA battery is implemented using LabVIEW 2015 software. The output information defining the condition of VRLA battery is displayed on front panel of LabVIEW and stored in MS Excel file with the time stamp at hard disk of host computer for further reliability analysis. Based on occurrence of major faults in VRLA battery, alert signal is send to intended users at both onsite and remote locations. To facilitate remote condition monitoring of VRLA battery, front panel information is continuously provided to remote user using web publishing tool of LabVIEW. Using proposed technique, the fault diagnosis of lead acid batteries in different battery applications can similarly be performed in non-invasive manner.

中文翻译：

---

基于红外热成像和模糊算法的阀控式铅酸蓄电池诊断系统

近年来，诸如红外热成像（IRT）之类的先进检查技术已以非接触，非破坏性和非侵入性的方式广泛用于电气设备的故障诊断。从IRT图像手动分类故障需要更多的时间和精力。在这项工作中，提出了一种用于VRLA电池预测性故障诊断的智能方案，以计划其预防性维护。使用IR相机在不同的放电周期下获取不间断电源应用中原始和老化的VRLA电池的IR图像。进行IR图像的图像处理以检测故障。为了对故障进行智能分类，开发了一种模糊推理系统。使用LabVIEW 2015软件实现了VRLA电池故障的自动诊断和分类的建议方案。定义VRLA电池状况的输出信息显示在LabVIEW的前面板上，并与时间戳一起存储在MS Excel文件中，该时间戳位于主机硬盘上，以进行进一步的可靠性分析。根据VRLA电池的重大故障的发生，警报信号会发送到现场和远程位置的目标用户。为便于远程监视VRLA电池的状况，使用LabVIEW的Web发布工具可连续向远程用户提供前面板信息。使用提出的技术，可以以非侵入方式类似地执行不同电池应用中的铅酸电池的故障诊断。