An Autonomous Electrical Signature Analysis-Based Method for Faults Monitoring in Industrial Motors

Rotating machines are widely used in industries, manufacturing, and oil and gas plants as a critical component for process availability. The inadvertent failure of these rotating machines causes significant process downtime and incurs higher repair costs and loss of revenue. These failures can belong to electrical, thermal, or mechanical fault categories. Early detection of all these faults is very much critical for avoiding complete failure of these machines and requires continuous 24/7 online monitoring using sensors or intelligent electronic devices (IEDs). However, an affordable low-cost and efficient online monitoring is desired to practice specifically for medium-voltage (MV) machines which have a larger install base. Electrical signature analysis (ESA) technology offers such flexibility and requires measuring just current and/or voltage at a motor control panel for machine health diagnosis, unlike vibration analysis (VA) requiring installation of sensors and its wiring on the machine. Furthermore, an intelligent, self-reliable, and autonomous ESA procedure is required for monitoring the machines due to the lack of ESA standards in practice unlike for VA. This article proposes a new autonomous electrical signature analysis (AESA)-based measurement technique implemented in IED, that is, Protection Relay, offering 24/7 online monitoring. The proposed technique implemented in Relay not only avoids dependence on standalone ESA hardware but also provides earlier detection of failures using peak and energy magnitudes computation approach at fault frequencies. To validate the proposed method, various tests were performed on the actual 1000- and 300-HP motors with/without mechanical faults in a machine repair shop and the results are discussed. The performance of the proposed method is also compared with the commercially available third-party ESA device results proving the efficacy.