The cost of unplanned equipment downtime in Indian manufacturing is estimated at ₹1 lakh per hour for a typical production line — and significantly higher for capital-intensive continuous process industries like petrochemicals and steel. Against this baseline, the economics of predictive maintenance are compelling: sensors cost thousands, analytics platforms cost lakhs, but the downtime they prevent costs crores.
Predictive maintenance (PdM) uses sensor data from equipment to detect early signs of degradation before failure occurs, enabling maintenance to be scheduled at a convenient time with appropriate parts and personnel in place. This is qualitatively different from both reactive maintenance (fix it when it breaks) and preventive maintenance (replace it on a schedule regardless of condition).
The sensor types relevant to mechanical equipment predictive maintenance are vibration sensors (detecting bearing wear, imbalance, misalignment), acoustic emission sensors (detecting micro-cracking in materials), temperature sensors (detecting lubrication failure, electrical resistance increases), and oil analysis (detecting metallic particles from wear). The specific sensor combination depends on the failure modes relevant to each equipment type.
The analytics layer translates raw sensor signals into maintenance recommendations. For simpler cases, threshold-based rules — alert when vibration amplitude exceeds 12mm/s — provide value immediately. For complex, multivariate failure modes, machine learning models trained on historical failure data provide earlier and more accurate warnings. Random forest and LSTM (long short-term memory) neural network models have both been deployed successfully in production PdM applications.
A full PdM programme requires three things beyond technology: a baseline of normal equipment operation from which anomalies are detected, a maintenance management system that converts PdM alerts into work orders and parts procurement, and a feedback loop that records actual failure causes when maintenance is performed, continuously improving the prediction model.
Indian manufacturers that have implemented mature PdM programmes report maintenance cost reductions of twenty-five to thirty percent and unplanned downtime reductions of fifty to sixty percent.