Prediction for Preventive Maintenance Using Multiple Linear Regression

Preventive maintenance plays an important function in optimizing problems regarding damages of machinery that often arise in the industrial environment, which reflects huge losses for enterprises. These analyses include various factors such as high ambient temperatures, fast machine revolving speed, poor estimation of torque figures, etc. This study aims to introduce preventive analytical results based on machine conditions that have the potential to develop damage. To attain this objective, we will carry out the analysis using a multiple linear regression analysis (MLRA) approach. in which a mathematical model is used for shaping and concluding prevention needs. The study starts by demonstrating an inclusive foundation for accumulating and preprocessing datasets, guaranteeing the dependability and genuineness of the recommendation variables. Key performance indicators (KPIs) in the way that system custom hours, unsteady levels, temperature alternatives, and support archives are collected into the deterioration model to anticipate the prediction for preventive conduct. The study starts by demonstrating an inclusive foundation for accumulating and preprocessing datasets, guaranteeing the dependability and genuineness of the recommendation variables. Key performance indicators (KPIs) in the way that system custom hours, unsteady levels, temperature alternatives, and support archives are collected into the deterioration model to anticipate the prediction for preventive conduct. The coefficients descended from the model support observations of each predictor, preservation groups to supply instructions efficiently. The results manifest that diversified deterioration reasoning can correctly forecast preventive necessities, lowering accidental spare time by 30% and maintenance costs by 20%. Furthermore, the unification of MLRA is accompanying actual plans to lower the predicting cost and admitting for vital adaptations established by current operating environments.