A Summability Assisted Machine Learning Framework for Predictive Maintenance of Industrial Equipment

Purpose: Industrial Predictive Maintenance (PdM) relies on sensor streams that are noisy, irregular, and affected by changing operating regimes, which can cause machine learning models to overreact to transient spikes or miss slow drift. This study develops a summability assisted machine learning framework that converts noisy time series into stable, interpretable features before prediction. Research Methodology: The study uses a methodological design that combines a literature synthesis, mathematical definitions of finite Cesàro means, weighted means, cumulative and robust summaries, a leakage resistant evaluation protocol, a cost sensitive decision rule, and simulation based illustrations on a synthetic pump record. Results: On the synthetic record, an adjacent window difference and a clipped cumulative sum detected a slow drift that a single sample limit missed while ignoring an isolated spike. Window length governed a clear trade off between false alerts and detection delay, and a worked example showed that a calibrated failure probability of 0.18 justifies inspection when the break even probability is 0.048. Conclusions: Summability features are a low cost and interpretable complement to raw signals, but their value must be demonstrated for each asset class and decision horizon. Limitations: The framework is not empirically validated, and all numerical results come from synthetic data. Contributions: The study provides an implementable integration pattern, an evaluation protocol linking smoothing to maintenance decisions, and a real data anchor based on the Intelligent Maintenance Systems (IMS) bearing tests for future empirical work.