Predictive Maintenance of Distributed Energy Sensors through Cloud Telemetry: An Audit-Ready Methods Framework

Abstract

Distributed energy-monitoring networks collect large amounts of telemetry, yet seasonal load changes can be mistaken for equipment faults. The difficulty is particularly visible when a system is transferred to a new setting. To address this, the study will distinguish actionable sensor degradation from benign demand fluctuations using time-aware predictive maintenance rules. The research design centres on a prospective-style replay of multiseason sensor traces with independently logged fault and repair events. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast fixed engineering thresholds, statistical change detection and a guarded machine-learning classifier. The audit-focused extension preserves processing logs, model decisions and exception handling for independent review. Related enterprise-storage studies inform the assessment of operational reliability [1] and the deployment assumptions adopted for system-level comparison [2]. Assessment will focus on alerts per sensor-month, detection lead time, missed faults and unnecessary maintenance decisions. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address sensor dropout, firmware changes, class imbalance and network interruption during peak loads. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Uncertain outcomes would be adjudicated with a predefined rubric, rather than silently recoded as successes. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].