Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning

Vibration analysis is the process of evaluating the vibration characteristics of a machine or structure, typically with the goal of identifying any problems or abnormalities that may be present. Vibrations are often indicative of the health and performance of a machine or structure and can provide valuable information about the condition of certain components, such as bearings, gears, and motors. By analyzing the characteristics of vibrations, such as frequency, amplitude, and waveform, it is possible to identify potential problems or failures that may occur in the future. The analysis of vibration is often performed in the frequency domain since the pattern of abnormalities in this domain is more obvious than in the time domain. Vibration signals convey more information than others for predictive maintenance, a maintenance technique based on the condition of machines. Other techniques are oil (lubricant) analysis [1], infrared thermography [2], and sound pattern analysis [3-5]. Vibration and lubricant analysis were the most common techniques for predictive maintenance (PdM) [6]. PdM, which is developed in the 1970s, is an advancement of preventive maintenance, a time-based maintenance from the 1950s [7]. Vibration analysis is a key predictive maintenance technique (among others) since it can identify the problem of machines before they become too serious and cause unscheduled downtime [1].