4. Maintenance Strategies and Policy Selection

Introduction and Context

Maintenance policy determines how an organisation preserves the function, safety, quality, and economic value of its physical assets. It is not simply a choice between repairing equipment after failure and servicing it at fixed intervals. A maintenance policy expresses a managerial decision about when intervention should occur, what evidence should trigger it, what resources are required, and how much operational risk the organisation is willing to accept.

The choice is consequential because maintenance has two opposing risks. Excessive intervention increases labour, materials, planning, and downtime costs and may itself introduce defects. Insufficient intervention can produce unplanned outages, safety incidents, environmental harm, quality losses, and accelerated asset deterioration. The appropriate policy therefore depends on the relationship between failure behaviour, asset criticality, detectability of deterioration, consequence of failure, and the economics of intervention.

This chapter compares major maintenance strategies—reactive, corrective, preventive, predictive, condition-based, opportunistic, and risk-based maintenance—and develops a framework for selecting among them. The discussion assumes that maintenance is part of an integrated production system. Decisions must therefore reflect production schedules, spare-parts policies, process constraints, regulatory obligations, asset design, and organisational capability.

Learning Objectives

After studying this chapter, the student should be able to:

1. Distinguish the principal maintenance strategies and explain their appropriate uses.

2. Relate maintenance policy to failure behaviour, reliability, maintainability, and operational consequences.

3. Apply criticality, risk, cost, and condition information to policy selection.

4. Explain the relationship between preventive, predictive, and condition-based maintenance.

5. Evaluate opportunistic and risk-based maintenance in production environments.

6. Recognise common errors, implementation barriers, ethical issues, and digital developments in maintenance policy.

7. Recommend a balanced maintenance policy for a specific asset or asset system.

The Policy Decision: What Is Being Chosen?

A maintenance policy answers four connected questions: what maintenance action will be performed, when it will be performed, what information will trigger it, and what consequence is being controlled. A policy may apply to an individual component, a functional failure, an asset, or an entire system. For example, a pump may have a condition-based policy for bearings, a time-based policy for seals, a run-to-failure policy for a non-critical pressure gauge, and an opportunistic replacement policy when the production line is already shut down.

It is useful to distinguish maintenance strategy from maintenance policy. Strategy refers to the broad approach, such as preventive or predictive maintenance. Policy is the operational rule that implements the approach: “inspect vibration weekly and replace the bearing when the alarm threshold is exceeded,” or “replace the filter every 2,000 operating hours.” A policy should identify the responsible role, required tools, data, acceptance criteria, escalation process, and record to be created.

Maintenance decisions are also different from maintenance tactics. A strategy may be condition-based, while the tactic involves vibration analysis, infrared thermography, oil analysis, or motor-current signature analysis. Good policy design connects strategic intent with technically valid and executable work.

The Main Maintenance Strategies

Reactive or Run-to-Failure Maintenance

Reactive maintenance permits an item to operate until it fails, after which repair or replacement is undertaken. In its pure form, no planned intervention occurs before functional failure. The approach is reasonable when failure has low consequences, the item is inexpensive and easily replaceable, redundancy exists, and failure does not create unacceptable safety, environmental, quality, or production effects.

Examples include replacing a standard indicator lamp, a low-cost office exhaust fan, or a non-critical disposable filter when failure is immediately apparent. Run-to-failure is not automatically poor maintenance. It becomes poor practice when used without a conscious assessment of consequences. For a critical compressor, reactive maintenance may generate emergency labour, expedited procurement, secondary damage, and extended production loss.

Reactive maintenance has low routine maintenance cost but potentially high failure cost. Its hidden costs include disruption, overtime, lost output, poor schedule stability, and the risk that emergency work will be performed under unsafe conditions.

Corrective Maintenance

Corrective maintenance restores an asset after a detected defect, fault, or failure. The term is broader than reactive maintenance. Corrective work may follow complete functional failure, but it may also follow an inspection finding before failure occurs. Replacing a cracked coupling identified during an inspection is planned corrective maintenance; replacing a failed coupling during an emergency is unplanned corrective maintenance.

Corrective maintenance is appropriate when a defect can be safely allowed to remain temporarily, when the work can be planned before functional failure, or when preventive tasks do not provide sufficient value. Planned corrective maintenance is generally superior to emergency repair because it permits proper diagnosis, materials planning, isolation, skilled staffing, and coordination with production.

Preventive Maintenance

Preventive maintenance is performed at predetermined intervals or according to prescribed criteria to reduce the probability of failure or degradation of function. Time-based servicing, usage-based replacement, lubrication rounds, statutory inspections, and scheduled overhauls are common examples.

Preventive maintenance may be:

• Time-directed: performed after a calendar period, such as monthly lubrication.
• Usage-directed: performed after operating hours, cycles, kilometres, or production units.
• Age-renewal: replacing an item at a specified life regardless of observed condition.
• Scheduled restoration: returning an item to a specified performance standard.

The central assumption behind age-based preventive maintenance is that age or usage is meaningfully related to failure probability. This assumption is valid for some wear-out mechanisms but weak for many modern complex systems whose failures are random or driven by operating conditions. Excessive scheduled replacement can create “infant mortality” through installation errors and can waste useful component life.

Predictive Maintenance

Predictive maintenance uses measurements, historical data, models, or statistical techniques to forecast future equipment condition or the likely time to failure. It is often associated with condition monitoring, but prediction may also use process trends, reliability models, maintenance history, and machine-learning algorithms.

A predictive programme typically involves baseline measurement, data acquisition, feature extraction, diagnosis, prognosis, and a maintenance decision. For example, increasing vibration at a characteristic bearing frequency may support a diagnosis of bearing deterioration, while a trend model estimates when the defect will reach an unacceptable level.

Prediction does not eliminate uncertainty. Sensor error, changing loads, incomplete failure histories, and model drift can produce false alarms or missed failures. Predictive maintenance therefore requires a decision rule, not merely a dashboard. The organisation must define what action follows an alert and whether the warning interval is sufficient for planning.

Condition-Based Maintenance

Condition-based maintenance initiates maintenance when evidence indicates that an asset’s condition has crossed, or is approaching, a decision threshold. Evidence may be obtained through continuous sensors, periodic inspection, operator observation, laboratory analysis, or functional testing. The defining feature is that intervention depends on actual or inferred condition rather than solely on elapsed time.

Condition-based maintenance is especially valuable when deterioration is detectable before functional failure and when a useful warning period exists. The P–F interval is the time between the point at which a potential failure can first be detected (P) and the point at which functional failure occurs (F). An inspection interval should normally be sufficiently short to detect deterioration within the P–F interval, while the organisation must still have time to plan and execute corrective work.

Condition-based maintenance differs from predictive maintenance in emphasis. Condition-based maintenance is a decision policy based on condition evidence. Predictive maintenance is a broader analytical approach that may forecast future condition or failure. In practice, the terms overlap, but they should not be treated as identical.

Opportunistic Maintenance

Opportunistic maintenance uses an already available intervention opportunity to perform additional work. A planned shutdown, product changeover, equipment isolation, or neighbouring asset repair may provide access that would otherwise be expensive or disruptive.

Examples include replacing a pump seal while a common skid is isolated, inspecting adjacent pipework during a shutdown, or changing several similar components when one has reached its replacement point. Opportunistic maintenance can reduce access, setup, and isolation costs. However, it can also cause scope expansion, consume contingency time, and introduce unnecessary work. Opportunities should therefore be governed by risk, evidence, and resource availability rather than by the belief that “everything accessible should be replaced.”

Risk-Based Maintenance

Risk-based maintenance prioritises maintenance according to the combination of failure likelihood and consequence. Consequence may include safety, environmental, regulatory, production, quality, financial, or reputational effects. Risk-based maintenance does not constitute one physical maintenance technique; it is a decision framework for allocating attention and resources.

A simple representation is:

Risk = Probability of failure × Consequence of failure

In professional applications, probability and consequence may be represented by ranges, scenarios, or distributions rather than single numbers. Risk analysis may use a risk matrix, failure modes and effects analysis (FMEA), reliability-centred maintenance (RCM), fault-tree analysis, or quantitative probabilistic models.

Risk-based maintenance prevents a common resource-allocation error: spending equal effort on assets with radically different consequences. A low-cost valve in a hazardous service may deserve more rigorous controls than an expensive but redundant motor in a non-critical application.

Failure Behaviour and the Logic of Policy Selection

Maintenance policy should reflect how failure probability changes with age and operating conditions. Some components exhibit wear-out behaviour, in which failure probability increases with age. Time-based replacement can be effective in such cases, especially when the component has a reasonably consistent life distribution and replacement is less costly than failure.

Other items show approximately random failures. For these, age-based replacement may provide little benefit because a new component can fail almost as readily as an older one. Condition monitoring, functional testing, design improvement, redundancy, or run-to-failure may be more appropriate.

Some equipment has an early-life failure period caused by manufacturing defects, installation errors, or commissioning problems. A preventive replacement programme may not address this pattern and may even recreate the early-life risk. Quality assurance, commissioning tests, root-cause analysis, and improved installation practices are more relevant responses.

A failure is evident when it is immediately apparent to operators or control systems. It is hidden when it affects a protective or standby function without being apparent during normal operation. Hidden failures require periodic functional tests, proof tests, or independent monitoring. For protective systems, allowing the item to run to failure is unacceptable because the failure may remain undiscovered until a demand occurs.

The principal diagnostic questions are:

1. Is the failure consequence acceptable?

2. Is deterioration detectable before functional failure?

3. Is the P–F interval long enough for a practical inspection and response process?

4. Does age or usage predict failure probability?

5. Is the asset accessible for inspection or intervention?

6. Is redundancy available and reliable?

7. What is the cost of intervention compared with the expected cost of failure?

8. Can the organisation execute the selected policy consistently?

A Framework for Selecting Maintenance Policy

A sound selection process begins with defining the required function and performance standard. “The pump operates” is insufficient; the requirement may be a specified flow, pressure, quality condition, availability, or safety function. The analysis then identifies functional failures and the failure modes that produce them.

The next stage is criticality assessment. Criticality is not simply equipment price. It reflects the consequence of losing the function and may include personnel exposure, environmental release, production interruption, product contamination, legal breach, repair duration, and lack of standby capacity. Criticality categories should be defined using organisational risk appetite and regulatory requirements.

Failure-mode analysis then evaluates the likely causes, effects, existing controls, detectability, and appropriate maintenance task. The following logic is useful:

• If failure has negligible consequence and the item is economical to replace, use run-to-failure.
• If failure is consequential but deterioration is detectable with adequate warning, use condition-based or predictive maintenance.
• If failure probability increases predictably with age, consider time- or usage-based preventive maintenance.
• If failure is hidden, use periodic functional testing or a suitable protective strategy.
• If no effective maintenance task can reduce the failure risk, consider redesign, redundancy, procedural control, or acceptance of residual risk.
• If access is costly and a shutdown is already planned, evaluate opportunistic work.

Economic evaluation should compare the total cost of alternatives, not only direct maintenance expenditure. A simplified expected annual cost can be expressed as:

Expected cost = planned maintenance cost + expected failure frequency × consequence cost

For a preventive action, consequence cost should include production loss, emergency response, secondary damage, safety controls, and restoration. For condition monitoring, include sensors, software, analysis, training, data management, and response labour. Net present value, life-cycle cost, simulation, and sensitivity analysis may be used for major capital assets.

The decision should finally be validated against organisational capability. A technically excellent policy fails if sensors are not calibrated, alerts are not reviewed, spare parts are unavailable, work orders are poorly planned, or technicians lack the required competence. Policy selection is therefore both a reliability decision and an operating-model decision.

Integrating Strategies in an Asset Portfolio

An asset rarely requires one universal policy. A production line may combine strategies according to function and failure mode. A critical gearbox may receive online vibration monitoring; a safety interlock may receive proof testing; a standard guard switch may be replaced when defective; lubrication may be scheduled by operating hours; and inspection of surrounding components may be performed during an annual shutdown.

This mixed strategy is often more rational than attempting to make every asset “predictive.” The objective is not maximum technology but optimum control of risk and life-cycle cost. Maintenance plans should identify interfaces between policies. For example, a condition-monitoring alert must create a planned corrective work order, while an opportunistic replacement must not invalidate the monitoring baseline or warranty conditions.

A Practical Decision Matrix

| Decision factor | Policy tendency |

|---|---|

| Low consequence, low replacement cost | Run-to-failure |

| Defect can be safely planned before failure | Planned corrective |

| Predictable age-related wear | Time- or usage-based preventive |

| Detectable deterioration and useful warning period | Condition-based or predictive |

| Hidden protective function | Periodic proof testing or functional inspection |

| High access/setup cost and planned shutdown available | Opportunistic maintenance |

| High safety, environmental, or production consequence | Risk-based prioritisation, often combined with preventive or condition-based tasks |

| No effective maintenance task | Redesign, redundancy, procedural control, or managed residual risk |

The matrix is a starting point, not an automatic answer. Evidence quality, uncertainty, and the cost of false alarms must be considered.

Application in Production and Service Organisations

In continuous-process industries, unplanned shutdowns may affect upstream and downstream operations. Policy selection must therefore consider bottleneck status, inventory buffers, process safety, restart complexity, and the possibility of cascading failures. In batch production, changeovers and campaign breaks create valuable opportunities for inspection and planned corrective work.

In discrete manufacturing, maintenance policies should reflect the distinction between a bottleneck machine and a non-bottleneck machine. A failure on a non-bottleneck may be absorbed by scheduling flexibility, whereas an identical failure on a bottleneck can stop the entire plant. In utilities, hospitals, transport, mining, and facilities management, service continuity and safety may dominate direct repair cost.

Managers should monitor whether the selected policy produces the intended result. Relevant measures include emergency-work percentage, schedule compliance, planned-to-unplanned work ratio, repeat failures, condition-monitoring alert precision, preventive-maintenance effectiveness, mean time between failures, mean time to restore function, maintenance cost per unit of output, and risk-reduction achieved. Metrics should support learning rather than encourage technicians to defer necessary work or close work orders inaccurately.

Digital Technologies and Analytical Development

Sensors connected through industrial networks enable continuous measurement of vibration, temperature, pressure, electrical characteristics, lubricant condition, and energy consumption. Computerised maintenance management systems (CMMS) and enterprise asset management systems (EAM) link asset registers, work orders, bills of material, inspections, costs, and failure histories.

Analytics can identify abnormal trends, classify failure modes, estimate remaining useful life, and prioritise work. Machine-learning models may be useful where large, reliable, representative data sets exist. Nevertheless, an algorithm cannot compensate for an inaccurate asset hierarchy, inconsistent failure codes, missing operating context, or unexecuted recommendations. Explainability is particularly important for safety-critical decisions.

Digital systems introduce governance requirements: cybersecurity, access control, data ownership, model validation, sensor calibration, retention rules, and human accountability. A predictive alert should support professional judgement, not obscure it. Automation should also be tested for bias toward assets with abundant data, since poorly instrumented assets may be important but digitally invisible.

Challenges and Common Errors

A frequent error is selecting preventive maintenance because it appears safer, without testing whether age-based intervention actually reduces failure probability. Another is treating every sensor alarm as a maintenance order. Alarm thresholds should be linked to failure mechanisms, operating state, confidence level, and response time.

Organisations also confuse activity with effectiveness. A high percentage of scheduled tasks completed does not prove that the tasks are necessary or that risk is controlled. Poorly defined job plans, weak defect elimination, inadequate spare-parts quality, and repeated temporary repairs may undermine any strategy.

Other challenges include:

• unreliable failure and cost data;
• weak ownership between operations, engineering, and maintenance;
• lack of time for analysis and planning;
• excessive scope during shutdowns;
• dependence on vendors without internal competence;
• ignoring human factors and maintainability during design;
• failure to review policies after process, load, technology, or regulatory changes.

Maintenance policy should be periodically reviewed using evidence from failures, inspections, near misses, cost trends, and changes in production conditions. A policy that was appropriate at commissioning may become unsuitable after a capacity increase or process modification.

Professional, Ethical, and Governance Considerations

Maintenance personnel and managers have a professional obligation to protect people, the environment, product users, and organisational assets. Cost reduction cannot justify bypassing statutory inspections, safety devices, environmental controls, or manufacturer limitations. Where risk cannot be reduced to an acceptable level, the issue should be escalated rather than concealed through optimistic assumptions.

Competence management is essential. Personnel performing vibration analysis, electrical testing, pressure-system inspection, or safety-system proof testing require appropriate qualifications and supervision. Records must be accurate because maintenance history supports safety decisions, warranties, audits, and future reliability analysis.

Governance should define who may change alarm limits, defer a preventive task, approve a risk acceptance, or override an automated recommendation. Deferrals should have an expiry date, compensating controls, and managerial approval. These controls help prevent gradual normalisation of deviance, in which unsafe temporary practices become accepted as routine.

Illustrative Case: Selecting Policies for a Packaging Line

Background

A fictional food-packaging plant operates a high-speed line consisting of a conveyor system, filling machine, sealing unit, compressed-air system, and product-detection safeguards. The line is a production bottleneck. Management observes that maintenance expenditure is rising, yet unplanned stoppages continue.

Situation and Problem

The plant uses a uniform monthly preventive-maintenance schedule. Technicians replace several bearings and belts regardless of condition. Some low-cost sensors are repaired only after failure. The sealing unit has suffered two unexpected stoppages caused by bearing deterioration, while a safety interlock has been found unavailable during a test. Production managers propose reducing preventive work to lower cost; the safety manager requests more frequent inspections.

Analysis

The team performs a criticality and failure-mode review. The sealing-unit bearings have a detectable vibration trend and a warning period of approximately several operating days, making condition-based maintenance feasible. Fixed-interval bearing replacement is not justified because operating load varies between products and premature replacements have introduced installation defects.

The safety interlock is a hidden protective function. Its failure may not be visible during normal production, so run-to-failure is unacceptable. A documented proof test at a defined interval, with immediate corrective action for failure, is selected.

A conveyor idler is inexpensive, accessible, and non-critical because a parallel path exists. Run-to-failure is accepted, provided spare idlers are stocked and replacement work is safe. The compressed-air dryer is critical to product quality, but its filter condition can be monitored through differential pressure. A condition-based task is chosen, with a time-based maximum replacement interval as a safeguard.

During the planned quarterly line shutdown, the team decides to inspect adjacent guards, cable routes, and lubrication points opportunistically. The scope is limited to documented defects and high-risk findings to avoid uncontrolled shutdown expansion.

Strategy and Implementation

The revised policy combines condition monitoring for sealing-unit bearings, proof testing for the interlock, run-to-failure for selected conveyor idlers, differential-pressure monitoring for the dryer, and opportunistic inspection during scheduled shutdowns. The CMMS is configured to create planned corrective work orders when vibration thresholds are reached. Alarm levels are reviewed by a reliability engineer and the equipment manufacturer.

Technicians receive training in sensor installation, data interpretation, and defect coding. Operations personnel are instructed to report unusual noise, temperature, leakage, and product-quality changes. A governance rule requires safety-interlock failures to be escalated immediately and prohibits indefinite deferral.

Outcome

The plant’s maintenance cost does not fall simply because preventive tasks are removed; instead, labour is redirected from low-value routine replacement to analysis, planning, and targeted intervention. Emergency stoppages decline, and the safety interlock receives documented assurance. The case demonstrates that an effective policy portfolio is selective: different failure modes on the same production line require different strategies.

Learning Points

1. Asset criticality must be assessed at the functional level.

2. Condition-based maintenance requires a detectable failure-development process and a usable warning interval.

3. Hidden protective failures require proof testing or equivalent assurance.

4. Run-to-failure can be rational when consequence, replacement cost, and redundancy are favourable.

5. Opportunistic maintenance should be controlled by risk and scope discipline.

6. Policy changes must be supported by data, competence, work management, and governance.

Best-Practice Guidelines

Effective maintenance policy selection generally follows these principles:

• Start with required function and failure consequence, not with a preferred technology.
• Use RCM or FMEA logic to connect failure modes with tasks.
• Choose time-based maintenance only where age or usage is meaningfully related to failure.
• Use condition monitoring when deterioration is detectable and action can be completed before failure.
• Treat predictive analytics as a decision-support capability, not as an automatic maintenance solution.
• Separate hidden-function testing from ordinary visual inspection.
• Include production, safety, quality, engineering, procurement, and maintenance stakeholders.
• Evaluate total life-cycle cost and risk, including secondary damage and lost production.
• Define thresholds, response times, responsibilities, and escalation rules.
• Review policies after failures, process changes, major modifications, and changes in operating context.

Chapter Summary

Maintenance policy selection is a strategic decision about controlling asset risk and life-cycle cost. Reactive or run-to-failure maintenance is suitable for low-consequence, inexpensive, replaceable items, while corrective maintenance may be planned or unplanned depending on when the defect is addressed. Preventive maintenance is effective when age or usage predicts deterioration, but indiscriminate scheduled replacement can waste resources and introduce defects. Condition-based maintenance uses evidence of actual condition; predictive maintenance extends this idea through forecasting and analytical models. Opportunistic maintenance exploits planned access, and risk-based maintenance prioritises resources according to probability and consequence.

The appropriate policy depends on criticality, failure behaviour, detectability, the P–F interval, redundancy, cost, maintainability, operational consequences, and organisational capability. Strong programmes combine several strategies across an asset portfolio. Digital tools can improve detection and prediction, but only when supported by reliable data, competent people, governance, and decisive work processes. The central professional principle is to select the simplest effective policy that reduces unacceptable risk at an economically defensible life-cycle cost.

Key Terms

Reactive maintenance: Maintenance performed after an item fails or loses required function.

Corrective maintenance: Action taken to restore an item after a defect, fault, or failure is identified.

Preventive maintenance: Maintenance performed at predetermined intervals or criteria to reduce failure probability or degradation.

Time-based maintenance: Preventive maintenance triggered by calendar time.

Usage-based maintenance: Preventive maintenance triggered by operating hours, cycles, distance, or output.

Predictive maintenance: Use of data, models, and trends to forecast condition or future failure.

Condition-based maintenance: Maintenance initiated by evidence that asset condition has reached a defined decision point.

Opportunistic maintenance: Additional maintenance performed when access or isolation is already available.

Risk-based maintenance: Allocation of maintenance effort according to failure likelihood and consequence.

Criticality: Relative importance of an asset or function based on the consequences of failure.

Functional failure: Inability of an asset to perform a required function to the specified standard.

Hidden failure: Failure not evident during normal operation, often affecting protective or standby functions.

P–F interval: Time between detectable potential failure and functional failure.

Failure mode: Specific manner in which an item fails or loses its required function.

Failure consequence: Effect of a failure on safety, environment, production, quality, cost, or compliance.

FMEA: Structured analysis of failure modes, effects, causes, and controls.

Reliability-centred maintenance: A systematic method for selecting maintenance tasks according to functions, failures, consequences, and applicable controls.

Proof test: A planned test intended to reveal hidden failures in a protective or standby function.

Remaining useful life: Estimated operating time before an asset can no longer meet its required function.

Review Questions

Short Answer Questions

1. How does planned corrective maintenance differ from reactive maintenance?

2. Define the P–F interval and explain its importance.

3. What is meant by a hidden failure?

4. State two conditions under which run-to-failure may be appropriate.

5. Why is risk-based maintenance not itself a single physical maintenance technique?

Long Answer Questions

1. Compare preventive, predictive, and condition-based maintenance, including their assumptions, benefits, and limitations.

2. Explain how failure behaviour influences the choice between age-based replacement and condition monitoring.

3. Describe a structured framework for selecting a maintenance policy for a critical production asset.

4. Discuss the role of opportunistic maintenance in shutdown and changeover planning.

5. Explain how digital technologies can improve maintenance policy while creating new governance risks.

Analytical / Application Questions

1. A standby emergency pump is rarely operated, but its failure may cause a serious environmental incident. Recommend a maintenance policy and justify it using hidden-failure and risk-based reasoning.

2. A motor bearing shows a gradual increase in vibration, but the plant has no process for reviewing alerts or scheduling corrective work. Should the organisation immediately invest in predictive analytics? Analyse the organisational and technical conditions required.

3. A non-bottleneck conveyor uses inexpensive, interchangeable rollers, while a bottleneck conveyor uses costly rollers whose failure stops production. Develop different policies for the two conveyors and explain the effect of redundancy, consequence, and total cost.

22. Multiple Choice Questions with Answers

Question 1

Which situation most strongly supports a run-to-failure policy?

A. A safety interlock whose failure is hidden during normal operation

B. An inexpensive, readily replaceable component with negligible failure consequence

C. A bottleneck gearbox with a detectable vibration trend

D. A pressure vessel subject to statutory inspection

Correct Answer: B. An inexpensive, readily replaceable component with negligible failure consequence

Question 2

What distinguishes condition-based maintenance from time-based preventive maintenance?

A. Condition-based maintenance is performed only after complete failure

B. Condition-based maintenance uses actual or inferred condition to trigger action

C. Condition-based maintenance never requires inspection intervals

D. Condition-based maintenance is limited to machine-learning systems

Correct Answer: B. Condition-based maintenance uses actual or inferred condition to trigger action

Question 3

The P–F interval is the period between:

A. Preventive maintenance and corrective maintenance

B. Purchase of an asset and its commissioning

C. Detectable potential failure and functional failure

D. Two consecutive functional failures

Correct Answer: C. Detectable potential failure and functional failure

Question 4

Which asset is most likely to require proof testing?

A. A low-cost indicator lamp

B. A redundant conveyor roller

C. A hidden protective shutdown device

D. A replaceable office fan

Correct Answer: C. A hidden protective shutdown device

Question 5

Why can fixed-interval replacement be ineffective for randomly failing components?

A. Random failures are always visible before they occur

B. Age or usage may not meaningfully predict their failure probability

C. Fixed intervals eliminate all maintenance costs

D. Random failures occur only in new equipment

Correct Answer: B. Age or usage may not meaningfully predict their failure probability

Question 6

A planned shutdown provides access to a pump skid. Replacing an adjacent deteriorated seal during that shutdown is an example of:

A. Opportunistic maintenance

B. Reactive maintenance

C. Hidden-failure maintenance

D. Failure acceptance

Correct Answer: A. Opportunistic maintenance

Question 7

Risk-based maintenance primarily prioritises work according to:

A. Equipment purchase price alone

B. Technician preference and convenience

C. Probability and consequence of failure

D. The number of sensors installed

Correct Answer: C. Probability and consequence of failure

Question 8

Which condition is essential for a useful condition-monitoring programme?

A. Every alarm must immediately stop production

B. There must be sufficient warning time to plan and execute action

C. All components must be replaced at the same interval

D. Historical data are unnecessary

Correct Answer: B. There must be sufficient warning time to plan and execute action

Question 9

What is a major danger of uncontrolled opportunistic maintenance during shutdowns?

A. It always reduces inspection quality

B. It can expand scope, consume contingency time, and introduce unnecessary work

C. It prevents all corrective work

D. It eliminates the need for risk assessment

Correct Answer: B. It can expand scope, consume contingency time, and introduce unnecessary work

Question 10

A predictive-maintenance alert has little value when:

A. The alert is linked to a defined response process

B. The failure mode has a detectable deterioration trend

C. The organisation cannot interpret the alert or execute the resulting work

D. The measurement baseline is reliable

Correct Answer: C. The organisation cannot interpret the alert or execute the resulting work