Motor fault diagnosis scheme by machine noise integration and signal similarity measurement

Abstract

In general, when an unusual phenomenon occurs in a device in a ship environment, it is detected by an alarm monitoring system (AMS) through a sensor, and its status and cause are immediately transmitted to the ship crew by an alarm. However, a specific mechanical fault is difficult to detect in the AMS; as such, the diagnosis of mechanical faults in the ship environment is often dependent on the subjective judgment of the crew. Therefore, if the noise generated from the machine can be collected, compared, and analyzed using information technology, the condition can be diagnosed instead of sounding an alarm to prevent damage to the machine. Herein, we propose two techniques for listening to noise from a machine that can categorize noise by a specific factor, extract features, differentiate noise from a normal machine noise, and diagnose noise as a failure. The two implemented techniques are the noise integration and signal similarity measurement techniques. These techniques are studied through motor noise measurement and analysis, and this paper focuses on motor failure diagnosis. The noise integration technique measures and analyzes the mechanical noise, and diagnosis is performed by extracting the noise integral value. The signal similarity measurement technique compares the similarity between the measured noise and the steady-state operating sound to perform a diagnosis. An experiment is conducted by creating an environment in which noises of various devices are mixed in a manner similar to the situation on a ship. It is discovered that the characteristics of the individual fault applied to the experiment are discernible. Hence, an effective fault diagnosis technique is devised by considering this analysis result and several combined factors.