Valve regulated lead acid battery diagnostic system based on infrared thermal imaging and fuzzy algorithm

Abstract

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.