Log In
Log In
Led by Senior Rotating Equipment Engineer Simulacrum
Condition monitoring and predictive maintenance from RCM and P-F interval through vibration analysis, oil analysis, thermography, ultrasound, acoustic emission, data acquisition, signal processing, AI/ML, remote monitoring, and cost-benefit analysis.
Led by Senior Rotating Equipment Engineer Simulacrum
The question
Before condition monitoring, maintenance was either run-to-failure or overhaul-on-schedule — studies show 70–90% of equipment overhauled on schedule has no degradation at the time. This module covers the four maintenance strategies (reactive through proactive), the RCM decision logic for selecting the correct strategy per failure mode, the P-F interval concept and the half-P-F monitoring frequency rule, six condition monitoring techniques (vibration, oil, thermography, ultrasound, acoustic emission, MCSA) with the faults each detects, and establishing the programme with baselines, three-level alarms, and monitoring routes.
Outcome
The student can describe the four strategies, apply the RCM decision logic, explain the P-F interval and half-P-F rule, and describe six CM techniques. (CM fundamentals)
Sub-units
Led by Senior Rotating Equipment Engineer Simulacrum
The question
Vibration analysis is the most powerful CM technique for rotating equipment — every mechanical fault produces a characteristic frequency pattern. This module covers vibration fundamentals (displacement, velocity, acceleration and when each is used), the FFT transformation from time to frequency domain, fault diagnosis from the spectrum (1x for unbalance, 2x for misalignment, BPFO/BPFI for bearing defects, gear mesh frequency, sub-synchronous oil whirl), envelope analysis for early-stage bearing detection weeks before direct spectrum, and overall vibration trending with ISO 10816 severity zones.
Outcome
The student can read a vibration spectrum to identify five fault types, explain envelope analysis, describe the four-stage bearing damage progression, and interpret an overall vibration trend. (Vibration analysis)
Sub-units
Led by Senior Rotating Equipment Engineer Simulacrum
The question
Vibration tells you about the rotating components — the complementary techniques extend coverage to the lubricant, the electrical system, the insulation, and the pressure vessels. This module covers oil analysis (wear metals, contamination, degradation — trending more important than absolute values), infrared thermography (electrical hot spots, bearing overheating, insulation failure, refractory damage), ultrasound (early bearing detection 1–3 months before vibration, leak detection, electrical discharge), acoustic emission for crack detection in pressure vessels under load, and MCSA for motor rotor fault detection without physical sensor access.
Outcome
The student can interpret oil analysis results, describe thermographic survey applications, explain ultrasound for early bearing detection and leak pinpointing, describe AE for pressure vessel testing, and explain MCSA's remote detection advantage. (Complementary CM techniques)
Sub-units
Led by Senior Instrumentation & Control Engineer Simulacrum (co-lead)
The question
The CM programme generates thousands of measurement points — the value depends on acquisition quality, signal processing, and alarm management. This module covers the data acquisition chain (sensor through signal conditioner, ADC, and analyser — with the Nyquist criterion and anti-aliasing), signal processing (bandpass filtering for envelope analysis, Hanning windowing to reduce spectral leakage, averaging to improve signal-to-noise), the three-level alarm structure (alert, alarm, trip) with absolute and relative thresholds, equipment-specific monitoring parameters for pumps, compressors, gearboxes, and motors, and CMMS integration for automatic work order generation.
Outcome
The student can describe the acquisition chain, explain three signal processing techniques, describe the three-level alarm structure, describe monitoring parameters for four equipment types, and explain the CMMS integration. (Data acquisition, signal processing, and alarms)
Sub-units
Led by Senior HSE Engineer Simulacrum
The question
Condition monitoring is being transformed by cheaper sensors, ubiquitous connectivity, and artificial intelligence that can read the data better than human analysts. This module covers three AI/ML applications (anomaly detection, fault classification at 85–95% accuracy, remaining useful life prediction), the four-layer IoT architecture (sensor, gateway, cloud, dashboard), cost-benefit analysis with a typical industry ROI of 5:1 to 10:1, performance dashboards and monthly reporting, and the five safety hazards for CM technicians working near operating machinery (entanglement, high temperature, noise, electrical, and working at height).
Outcome
The student can describe three AI/ML applications, describe the IoT architecture, calculate the CM programme ROI, describe the dashboard and report structure, and describe five safety hazards with their controls. (AI/ML, remote monitoring, cost-benefit, and safety)
Sub-units