Research on Mechanical Equipment Fault Diagnosis and Prediction Technology Based on Vibration Signal Analysis

This research delves into the field of fault detection and prognosis for mechanical systems, leveraging vibrational data analysis to enhance precision and efficiency in diagnosing issues within industrial machinery. By integrating sophisticated signal processing techniques like Wavelet Transform (WT) and Empirical Mode Decomposition (EMD), we conduct an in-depth examination of vibrational data from mechanical equipment to discern pertinent fault-related characteristics. Leveraging these identified traits, the research employs machine learning methodologies, including Support Vector Machine (SVM) and Long Short Term Memory Network (LSTM), to develop models tailored for fault detection and forecasting. Experimental outcomes reveal that our devised model registers notable achievements in diagnosing and predicting defects in rolling bearings, boasting a diagnostic precision surpassing 97.0%. Remarkably, it can anticipate anomalous vibration patterns hours prior to fault onset, affording maintenance teams a crucial window for preventative measures. Beyond its scholarly merit, this investigation holds paramount practical relevance, offering insights to guide industrial operations and promising to bolster the secure and optimal performance of mechanical systems with innovative technological support.