Application of condition-based maintenance for electrical generators based on statistical control charts

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality. Moreover, heat monitoring surpassed vibration and noise in failure detection, where temperature control charts successfully identified failure. The overall results support the significant role of statistical charts in decision-making regarding condition-based maintenance for electrical equipment like generators. • Application of statistical control charts for condition-based maintenance of electrical generators. • Detecting dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. • Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process.