Equipment Failure History
The structured record of every equipment failure event — capturing failure date, asset identity, failure mode, root cause classification, affected components, time to repair, production impact, and the corrective action taken, linked to the associated work order and inspection findings.
Why This Object Matters for AI
AI cannot build predictive failure models, estimate remaining useful life, or identify chronic failure patterns without a structured failure history; without it, 'has this motor failed before and why' requires searching through years of paper work orders and technician notes.
Maintenance & Reliability Capacity Profile
Typical CMC levels for maintenance & reliability in Manufacturing organizations.
CMC Dimension Scenarios
What each CMC level looks like specifically for Equipment Failure History. Baseline level is highlighted.
Equipment failure knowledge lives in technicians' memories. When the reliability engineer asks 'has this motor failed before?' the answer is 'I think so, maybe two years ago — ask Tom, he was here then.' There's no written record of past failures, their causes, or what was done to fix them. Each failure is treated as a new event because there's no history to reference.
AI cannot build any predictive failure models because no failure history records exist in any system.
Start recording failures in any written format — even a logbook or spreadsheet with columns for date, equipment, what failed, and what was done.
Some failure records exist in work order descriptions — 'replaced motor on Line 3, burned out' — but they're buried in free-text fields with no consistent structure. The reliability engineer searching for 'all bearing failures on CNC machines' has to read through hundreds of work orders looking for keywords. Some failures are documented in detail; most are a single sentence. There's no distinction between a failure record and a routine maintenance note.
AI can perform keyword searches across work order text to find potential failure mentions but cannot reliably identify, classify, or trend failures because descriptions lack consistent structure and failure-specific categorization.
Create a dedicated failure reporting structure — separate failure records from routine work orders with required fields for failure mode, root cause category, affected component, time to repair, and production impact.
A structured failure reporting form exists with standardized fields: equipment ID, failure date, failure mode category, root cause classification, affected component, repair duration, and production impact hours. Reliability engineers enter failure records consistently. The data lives in a spreadsheet or basic database. The engineer can filter 'all motor failures in 2025' and get a clean list. But failure records are standalone — not linked to the work orders, condition data, or parts that tell the full story.
AI can perform basic failure trending — failure rates by equipment type, most common failure modes, mean time between failures. Cannot correlate failures with operating conditions, maintenance history, or parts quality because the failure records aren't linked to those systems.
Link failure records to the associated work orders, equipment asset records (with operating history), condition monitoring readings preceding the failure, and the parts that were replaced.
Failure records are in a CMMS or reliability database with enforced relationships. Each failure links to the associated work order, the equipment asset's complete history, the condition monitoring readings in the weeks before failure, the parts replaced, and the maintenance procedure followed. The reliability engineer can query 'show me all failures where vibration exceeded threshold before the failure occurred, and what was the lead time between the threshold breach and the actual failure?' — and get a structured answer.
AI can build data-driven failure prediction models correlating failure patterns with operating conditions, maintenance history, and component age. Root cause analysis is quantitative rather than anecdotal. Remaining useful life estimation becomes feasible.
Structure failure records with formal failure mode effects analysis (FMEA) ontology — mapping failure modes to specific component hierarchies, severity classifications, and causal chains with validated taxonomy codes.
Failure history is schema-driven with formal FMEA relationships. Every failure record maps to a component in the equipment BOM hierarchy, a failure mode from the RCM taxonomy, a severity/criticality classification, and the complete causal chain (contributing factors, root cause, immediate cause). An AI agent can ask 'what is the probability of a bearing failure on any high-speed spindle within the next 30 days based on current vibration signatures and historical failure patterns for similar equipment?' and compute the answer from structured records.
AI can perform comprehensive predictive reliability engineering — failure probability estimation, remaining useful life calculation, maintenance strategy optimization, and design feedback — from the formal failure knowledge model.
Implement real-time failure event streaming — failure detections, condition threshold breaches, and anomaly alerts publish as events the moment they're detected, not when a human enters them.
Failure records generate automatically in real-time from equipment condition monitoring. When sensors detect a failure event — sudden vibration spike, temperature excursion, current anomaly — the system automatically creates a failure record, classifies the likely failure mode based on the signature pattern, links it to the equipment's component hierarchy, and initiates the work order. The failure history is a living stream that documents itself without human failure reporting.
Fully autonomous failure detection, classification, and response. AI identifies failures the moment they occur, classifies them from sensor signatures, and initiates corrective action without waiting for human observation or reporting.
Ceiling of the CMC framework for this dimension.
Capabilities That Depend on Equipment Failure History
Other Objects in Maintenance & Reliability
Related business objects in the same function area.
Maintenance Work Order
EntityThe transactional record that authorizes and tracks a maintenance task — containing the target asset, problem description, work type (corrective, preventive, predictive), priority, assigned technician, parts consumed, labor hours, completion status, and root cause code upon closure.
Spare Parts Inventory
EntityThe managed stock of maintenance, repair, and operations (MRO) parts — including part numbers, criticality ratings, on-hand quantities, reorder points, lead times, interchangeability data, and the mapping of which parts serve which equipment assets.
Maintenance Procedure
EntityThe step-by-step instructions for performing a maintenance task on a specific asset type — including safety lockout/tagout requirements, tools needed, parts lists, torque specifications, inspection checkpoints, and expected completion time maintained by reliability engineers.
Lubrication Schedule and Specification
EntityThe managed program defining lubrication requirements for each asset — specifying lubricant types, application points, quantities, frequencies, condition monitoring thresholds (viscosity, contamination), and the route maps that lubrication technicians follow on their rounds.
Equipment Health Score
EntityThe composite condition index maintained for each critical asset — aggregating sensor readings, inspection results, failure history, age, operating hours, and maintenance compliance into a normalized health score that reliability engineers use to prioritize attention and predict degradation trajectories.
Repair-versus-Replace Decision
DecisionThe recurring judgment point where maintenance and engineering evaluate whether to repair a degraded asset or replace it — weighing remaining useful life estimates, cumulative repair costs, replacement lead time, production impact, and capital budget availability against defined thresholds.
Maintenance Priority Decision
DecisionThe recurring judgment point where maintenance planners determine which work orders to execute first given constrained labor, parts, and production windows — applying criteria such as asset criticality, safety risk, production impact, regulatory deadline, and health score degradation rate.
Preventive Maintenance Schedule Rule
RuleThe codified logic that determines when preventive maintenance tasks are triggered for each asset class — including time-based intervals, usage-based thresholds (run hours, cycle counts), condition-based triggers, and the escalation rules when PMs are deferred beyond acceptable windows.
Failure Mode Classification Rule
RuleThe taxonomy and classification logic that standardizes how equipment failures are categorized — defining failure mode codes, cause codes, effect codes, and the hierarchical structure (asset class → component → failure mode → root cause) that ensures consistent coding across technicians and shifts.
Work Order Lifecycle Process
ProcessThe end-to-end maintenance workflow from work request initiation through planning, scheduling, execution, quality check, and closure — defining approval gates, parts staging requirements, permit-to-work handoffs, technician sign-off steps, and the feedback loop that updates failure history and health scores upon completion.
What Can Your Organization Deploy?
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