Local Maximum Acceleration Based Rotating Machinery Fault Classification Using KNN

Rotating machinery are continuously operated tools for power generation and mechanical applications. The smooth operation of these tools is fundamental in businesses to accomplish their profitability. The status of such machines can be monitored by continuously assessing their working parameters that aid to identify abnormal behaviors. Upon detection of abnormal behaviors, such machines can be early scheduled for maintenance. Conditioned Based Maintenance (CBM) is one such approach that continuously monitors the machine and recommends taking action before equipment fails. This approach uses monitoring parameters such as temperature, pressure, and vibration signals to minimize unnecessary breakdown and catastrophic failure. Vibration parameters based machine fault identification approach is one of the widely used methods in CBM. Various machine malfunctions can be predicted and detected based on energy at specific frequencies of vibration signals. Common faults such as unbalanced and misalignment are often identified by examining the operating frequencies and their harmonics. Based on machine malfunctions, the dominant differences are expected at these frequencies. Barring few dominating peaks, other peaks are not exactly located at harmonic speed, so it can be misleading to use information from only harmonic speed. Rather than just examining operating frequencies and their harmonics, we looked at the local peaks and identified their frequencies. The acceleration amplitude at the operating speed and the local maximum acceleration amplitude are selected as a vibration feature for the fault classification. The proposed KNN classifier demonstrated its reliability with an accuracy of over 96% for the tested data set of 25 samples.