A large manufacturing organization operating multiple production lines was facing a growing operational risk: unplanned equipment downtime was increasing year over year. Although the plant had preventive maintenance schedules and experienced maintenance teams, failures still occurred unexpectedly—often during peak production runs—leading to missed dispatch targets, overtime costs, and inconsistent product quality.
The client’s core pain points were clear:
Reactive breakdown cycles: Maintenance teams were spending too much time firefighting. Machines were being repaired after failure rather than before it.
Inefficient preventive maintenance: Fixed schedules led to unnecessary part replacements for healthy assets and missed early warnings for deteriorating ones.
Data fragmentation: Sensor signals, PLC/SCADA logs, maintenance records, and operator notes existed across different systems and formats. There was no single, trusted asset health view.
Limited early warning capability: Alarms were threshold-based and often triggered too late, while subtle patterns leading to failure were not detected.
Scaling complexity: The organization had tried small pilots, but scaling predictive maintenance across many asset types, lines, and plants required standardization, governance, and repeatable deployment.
The business needed a solution that could predict failures early, prioritize maintenance actions, reduce downtime, and work across diverse equipment—without creating operational burden or false alarms that teams would ignore.
Maayan Technologies implemented a production-grade predictive maintenance (PdM) program designed for accuracy, adoption, and scale—not just a proof-of-concept.
1) Asset Prioritization & Failure Mode Mapping
We started with a structured discovery to identify the “critical few” assets driving the majority of downtime cost—compressors, motors, pumps, gearboxes, spindle systems, conveyors, and utilities systems. For each, we defined key failure modes (bearing wear, misalignment, vibration anomalies, temperature drift, lubrication issues, electrical faults) and mapped the signals most correlated with early-stage degradation.
This ensured the system focused on what mattered most: preventing downtime events that impact throughput and quality.
2) Unified Data Pipeline: OT + IT Integration
We built a reliable data foundation by integrating multiple sources:
Real-time sensor streams (vibration, temperature, current, pressure, RPM)
PLC/SCADA events and machine state signals
Maintenance history from CMMS/EAM systems
Operator shift logs and incident notes
Data was cleaned, timestamp-aligned, and contextualized to create consistent “asset health datasets” per machine. This step eliminated the usual blocker in manufacturing AI—dirty, mismatched, and siloed data.
3) Hybrid Modeling Approach (Explainable + Accurate)
Instead of relying on a single algorithm, we used a hybrid approach built for industrial environments:
Anomaly detection models to flag early deviation from normal behavior (useful when failure labels are limited).
Supervised prediction models where historical failure data existed, enabling higher precision and estimated time-to-failure windows.
Feature engineering tuned for rotating equipment and process signals (trend slopes, vibration band energy, harmonics, temperature deltas, duty cycles).
Explainability layer to show “why” an alert triggered (e.g., increasing vibration amplitude + rising motor current under similar load).
This improved trust and reduced the “black-box” problem that often blocks adoption on the plant floor.
4) Real-Time Alerts + Maintenance Workflow Enablement
The system was operationalized with clear outputs:
Asset Health Score per machine and per subsystem
Risk ranking dashboard to prioritize the top assets requiring attention
Actionable alerts with recommended checks (alignment test, lubrication inspection, bearing replacement plan, thermal scan)
Integration into maintenance workflows so alerts converted into work orders with traceability (where feasible)
We also defined alert thresholds to reduce noise and configured escalation rules to ensure the right teams received the right information at the right time.
5) Scale Blueprint & Governance
To scale across lines and plants, we created a repeatable rollout kit:
Standard sensor and tagging guidelines
Model monitoring and drift checks
Change management playbook and role-based training
KPI tracking framework tied to downtime reduction and maintenance efficiency
This turned predictive maintenance from an experiment into a sustained operational capability.
After rollout, the manufacturing organization achieved measurable improvements across reliability, cost, and productivity:
Reduced unplanned downtime through early detection of failure patterns before breakdowns.
Higher maintenance efficiency by shifting from schedule-based replacements to condition-based interventions.
Improved throughput and dispatch reliability, especially during peak production cycles.
Better asset utilization and longer equipment life due to fewer catastrophic failures and optimized maintenance timing.
Faster root-cause analysis using explainable insights and consolidated asset history.
Scalable PdM foundation ready to expand to additional assets, plants, and new failure modes without rebuilding from scratch.
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