<![CDATA[Predictive maintenance aims to improve maintenance timing by converting condition signals into information that supports inspection, intervention, and scheduling decisions. This study provides a reproducible descriptive analysis of the two supplied files for an industrial-equipment monitoring exercise: 150 daily observations from five machines during 1-30 June 2024 and five supplied model-benchmark rows. The sensor file contains vibration, temperature, pressure, current, operating hours, a maintenance-alert prediction, and a same-day recorded status. An alert is treated as positive when its label is “maintenance required,” and a recorded breakdown is treated as positive for the descriptive comparison. The resulting same-day confusion matrix is TP = 6, FP = 10, TN = 134, and FN = 0. Accuracy is 93.33%, precision is 37.50%, recall is 100.00%, F1 is 54.55%, and the false-alarm rate is 6.94%. The high recall is accompanied by ten alerts on records marked normally, which constrains the operational interpretation of accuracy alone. The supplied benchmark table reports five algorithms, with LSTM having the highest listed accuracy and F1 score but also the longest listed training time; these values are reported as supplied and are not recomputed from the sensor file. Four source-derived figures examine alert timing, vibration-temperature patterns, the same-day outcome matrix, and the supplied benchmark trade-offs. The study does not train, tune, split, or test a new model, and it does not observe maintenance actions, downtime duration, or a causal comparison period. Accordingly, the results describe alignment between same-day labels in the supplied records; they do not prove that a deployed model reduced industrial equipment downtime. The contribution is a transparent reporting template that links sensor evidence, alert burden, and model-selection trade-offs while preserving this evidence boundary]]>
