108 Predictive Maintenance: Models and Rollout

108.5.1 Thermal Monitoring Architecture

Before thermal monitoring architecture, inspect Figure 108.2: Thermal Monitoring Architecture must be considered with 1. Thermal sensing. That visual pairing grounds thermal monitoring system architecture from sensing to alerts in named evidence.

At Thermal Monitoring Architecture in Figure 108.2, compare the diagram with 1. Thermal sensing; then locate Handheld camera for inspections. That labelled check bounds thermal monitoring system architecture from sensing to alerts. For thermal monitoring architecture, retain Handheld camera for inspections as evidence for the resulting choice.

Thermal monitoring architecture showing infrared sensing technologies feeding into analysis engine for baseline comparison, trending, and anomaly detection across electrical, mechanical, and process equipment applications.

108.5.2 Electrical Applications

• Hot spots on connections indicate high resistance
• Overheated components indicate overload
• Phase imbalance in motors
• Can detect problems 6-12 months in advance

108.5.3 Mechanical Applications

• Bearing overheating (friction)
• Belt misalignment (heat buildup)
• Lubrication issues (dry bearings)
• Coupling problems

108.5.4 Temperature Thresholds

108.6.1 ML Model Selection Decision Tree

Use Figure 108.3 to prepare the decision in ml model selection decision tree. The diagram names ML Model Selection Guide and Choose the model family that matches the maintenance, the two anchors needed to assess ml model selection decision tree for predictive maintenance.

Locate ML Model Selection Guide on Figure 108.3 before checking Choose the model family that matches the maintenance. The visual’s third anchor, Start here, completes ml model selection decision tree for predictive maintenance. Carry ML Model Selection Guide into ml model selection decision tree; use Start here as its limiting condition.

Use this decision tree to select the appropriate ML approach based on your available data and prediction goals.

108.6.2 Supervised Learning

Approach: Requires labeled failure data to train classifiers.

Algorithms:

• Random Forest, XGBoost for classification
• Neural networks for complex patterns

Output: “Will this bearing fail in next 30 days?” (Yes/No with probability)

Requirements:

• Historical failure data (dozens to hundreds of examples)
• Consistent sensor data leading up to failures
• Domain expertise to label failure modes

108.6.3 Unsupervised Learning

Approach: Learns normal operation without failure labels.

Algorithms:

• Autoencoders (reconstruction error indicates anomaly)
• Isolation Forests (detects outliers)
• One-class SVM

Output: “Is this vibration signature abnormal?” (Anomaly score)

Advantages:

• Works without historical failures
• Detects novel failure modes
• Good for rare events

108.6.4 Time-Series Forecasting

Use this time-series forecasting section as a guided decision record, not as a list to memorise. First identify the stated input, assumption, or scenario; then compare each option on the same units and time boundary. Next check which value changes the outcome and which evidence would reveal an invalid assumption. For time-series forecasting, the useful result is the reasoning chain: observed condition, governing constraint, calculation or classification, and operational consequence. Record that chain before choosing an answer or carrying a value into the next section. Where the panel supplies several choices, reject each distractor against the chapter’s named mechanism instead of relying on wording cues. Where it supplies a table or timeline, compare rows at like-for-like scale and preserve the difference between an early indication, an actionable threshold, and a final outcome. This turns time-series forecasting into evidence that can be reviewed, recalculated, and connected to the running design narrative.

Approach: Predicts remaining useful life (RUL) based on degradation trends.

Algorithms:

• LSTM neural networks
• Prophet (trend + seasonality)
• Gaussian Process Regression

Output: “How many hours/days until failure?” (RUL estimate with confidence interval)

Key metrics:

• Mean Absolute Error (MAE)
• Root Mean Square Error (RMSE)
• Percentage within 10%/20% tolerance

108.8.1 Cost Components

Investment costs (illustrative ranges; replace with local quotes):

• Sensors: $100-500 per motor (vibration, temperature)
• Gateways: $500-2,000 per zone
• Software: $50,000-500,000 (depending on scale)
• Integration: 2-5x hardware cost for brownfield
• Training: $1,000-5,000 per technician

Operating costs (illustrative ranges; replace with local contracts):

• Platform licensing: $10-50 per asset/month
• Connectivity: $5-20 per gateway/month
• Data storage: $0.02-0.05 per GB/month
• Analyst time: $50,000-100,000/year for dedicated resources

108.8.2 Benefit Categories

Direct savings:

• Reduced emergency repairs (labor + parts + expediting)
• Extended equipment life (deferred replacement)
• Lower spare parts inventory (order when needed)
• Reduced energy consumption (efficient equipment)

Indirect savings:

• Avoided production losses (unplanned downtime)
• Improved quality (equipment in specification)
• Reduced safety incidents (early warning of hazards)
• Better capital planning (known equipment condition)

108.8.3 Sample ROI Calculation

Use this sample roi calculation section as a guided decision record, not as a list to memorise. First identify the stated input, assumption, or scenario; then compare each option on the same units and time boundary. Next check which value changes the outcome and which evidence would reveal an invalid assumption. For sample roi calculation, the useful result is the reasoning chain: observed condition, governing constraint, calculation or classification, and operational consequence. Record that chain before choosing an answer or carrying a value into the next section. Where the panel supplies several choices, reject each distractor against the chapter’s named mechanism instead of relying on wording cues. Where it supplies a table or timeline, compare rows at like-for-like scale and preserve the difference between an early indication, an actionable threshold, and a final outcome. This turns sample roi calculation into evidence that can be reviewed, recalculated, and connected to the running design narrative.

Scenario: 100-motor manufacturing plant using editable planning assumptions.

With predictive maintenance:

html`<div style="margin: 20px 0;">
<h4 style="color: #2C3E50;">Investment Breakdown</h4>
${Plot.plot({
marginLeft: 100,
x: {label: "Cost ($)", grid: true},
y: {label: null},
marks: [
Plot.barX(roiBreakdown, {
y: "category",
x: "cost",
fill: "#3498DB",
tip: true,
title: d => `${d.category}: $${d.cost.toLocaleString()} (${d.percent}%)`
}),
Plot.text(roiBreakdown, {
y: "category",
x: "cost",
text: d => `$${(d.cost/1000).toFixed(0)}K`,
dx: -30,
fill: "white",
fontSize: 12,
fontWeight: "bold"
}),
Plot.ruleX([0])
]
})}
</div>`

108.9.1 Phase 1: Pilot (Months 1-6)

• Select 10-20 critical assets
• Deploy basic vibration and temperature sensors
• Establish data collection infrastructure
• Create baseline normal operation profiles
• Success metric: Detect one previously undetected issue

108.9.2 Phase 2: Expansion (Months 7-18)

• Expand to 50-100 assets
• Implement ML-based anomaly detection
• Integrate with CMMS for work order generation
• Train maintenance technicians on new tools
• Success metric: measured reduction in emergency work on pilot asset classes

108.9.3 Phase 3: Optimization (Months 19-36)

• Full facility coverage (all critical assets)
• Remaining useful life predictions
• Automated parts ordering
• Continuous model improvement
• Success metric: sustained planned-maintenance ratio and technician-confirmed alert quality