Maintenance Management Foundations

Introduction and Context

Maintenance is the management of technical assets throughout their operating life so that they deliver required performance safely, economically, and sustainably. In a production system, maintenance is not merely the repair of machines after breakdown. It is a coordinated managerial and engineering function involving asset selection, installation, inspection, lubrication, adjustment, replacement, modification, data analysis, and continuous improvement.

For production managers, maintenance has strategic importance because equipment condition directly affects throughput, product quality, delivery reliability, energy consumption, worker safety, environmental performance, and operating cost. A highly automated factory may have fewer operators but greater dependence on the availability and control of critical assets. Consequently, maintenance decisions influence both short-term production continuity and long-term competitiveness.

The central challenge is to determine what maintenance should be performed, when it should be performed, by whom, and at what cost. Excessive maintenance wastes labor, materials, and productive time; insufficient maintenance increases the probability and consequences of failure. Effective maintenance management therefore seeks an economically and operationally appropriate balance between risk, performance, and resource consumption.

Learning Objectives

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

1. Define maintenance and distinguish important maintenance terminology.
2. Explain the relationship among failure, fault, defect, downtime, reliability, maintainability, and availability.
3. Describe the evolution from reactive maintenance to preventive, predictive, proactive, and prescriptive approaches.
4. Relate maintenance performance to production, quality, safety, sustainability, and profitability.
5. Select an appropriate maintenance approach according to failure behavior, criticality, risk, and available information.
6. Evaluate maintenance decisions using operational and economic indicators.

1. The Maintenance Function in the Production System

A production system transforms inputs—materials, labor, energy, information, and capital—into products or services. Maintenance preserves or restores the capability of the physical assets that perform this transformation. These assets include machines, tools, buildings, utilities, software-controlled equipment, transportation systems, and safety-critical devices.

Maintenance has two fundamental purposes:

• Preservation: retaining equipment in a condition capable of performing its intended function.
• Restoration: returning equipment to that condition after deterioration or failure.

The phrase “good condition” must be interpreted functionally. A pump may still rotate but fail to deliver the required flow; a filling machine may operate but produce incorrect fill volumes; a safety interlock may appear intact but fail to interrupt hazardous motion. Maintenance must therefore be linked to required performance, not merely to physical appearance or the fact that an asset is running.

A modern maintenance function typically includes:

• asset hierarchy and technical documentation;
• maintenance strategy and task selection;
• work identification, planning, scheduling, and execution;
• spare-parts and materials management;
• condition monitoring and inspection;
• failure investigation and root-cause analysis;
• maintenance information systems;
• competence, safety, and contractor management;
• performance measurement and improvement.

Maintenance management is the process of directing these activities so that asset performance supports organizational objectives. Maintenance engineering supplies technical analysis; maintenance management integrates that analysis with production priorities, financial constraints, risk, people, and governance.

2. Essential Terminology

Asset, function, and functional failure

An asset is an item, system, or infrastructure resource that has potential or actual value to an organization. An asset may be physical, digital, or a combination of both. A production line, compressor, programmable logic controller, or plant cooling system can each be treated as an asset at an appropriate level of analysis.

A function is what an asset is required to do, including a specified performance standard. For example, a centrifugal pump may be required to deliver 80 m³/h at a specified pressure, while consuming no more than a defined amount of energy.

A functional failure occurs when an asset is unable to perform a required function to the specified standard. It is broader than complete destruction. Reduced speed, excessive vibration, inaccurate measurement, leakage, or inability to meet quality tolerances may all constitute functional failure.

A failure is the termination of the ability of an item to perform a required function. A fault is an abnormal condition that may indicate a failure or impending failure. A fault code, rising bearing temperature, or unusual vibration may exist before complete loss of function.

A defect is a nonconformity or imperfection that may or may not yet have caused functional failure. The distinction is useful because maintenance should often act while an abnormal condition is still a defect or fault, before it develops into a consequential failure.

Breakdown and downtime

A breakdown is a failure that results in loss of function and normally requires corrective action. Downtime is the period during which an asset is unavailable for production or unable to perform its required function. It may include waiting for diagnosis, permits, parts, labor, access, repair, testing, and restart—not only the hands-on repair time.

Corrective maintenance is maintenance performed after a fault has been detected, intended to restore the required function. It may be planned or unplanned. A planned corrective task can be scheduled after a noncritical defect is identified; an emergency repair following an unexpected trip is unplanned corrective maintenance.

Repair restores an item to an operating condition. Overhaul is a comprehensive examination and restoration, often involving replacement of worn components. Modification changes the design or configuration to improve performance, remove a failure cause, or meet a new requirement.

Reliability, maintainability, and availability

Reliability is the probability that an item performs a required function under stated conditions for a specified time interval. Reliability is therefore conditional on function, operating environment, time, and performance criteria.

Maintainability is the probability that a failed item can be restored to a specified condition within a given time, using stated procedures and resources. Design features such as accessibility, modularity, diagnostics, standard fasteners, and safe isolation influence maintainability.

Availability expresses the proportion of time an asset is capable of performing its required function. Different availability definitions exist. A commonly used steady-state inherent availability is:

where A_i is inherent availability, MTBF is mean time between failures, and MTTR is mean time to repair. Both time measures must use the same units. This expression assumes a repairable item, statistically stable failure and repair behavior, and that only corrective repair time is considered; delays for logistics, administration, and planned maintenance are excluded. Operational or achieved availability may include such delays and scheduled maintenance, so the definition must always be stated.

MTBF is a useful average for repairable items, but it is not a guarantee that a failure will occur only after that interval. MTTR is an average, not a fixed repair duration. A manager should avoid comparing these indicators without checking data quality, asset population, operating context, and definitions.

Maintenance-induced and latent conditions

A maintenance-induced failure is caused or accelerated by maintenance activity, for example incorrect assembly, contamination, over-tightening, wrong lubrication, or failure to restore a protective device. A latent failure is not immediately evident during normal operation, commonly occurring in protective or standby systems. Periodic functional testing may be needed to reveal it.

A failure mode describes how an item fails, such as bearing seizure, seal leakage, electrical insulation breakdown, or sensor drift. A failure cause is the underlying reason, such as inadequate lubrication, overload, misalignment, contamination, design weakness, or incorrect procedure. A failure effect describes what happens locally and at system level. A failure consequence is the resulting impact on safety, production, quality, environment, cost, or reputation.

3. Failure Consequences and Asset Criticality

Not all failures deserve the same response. A failed decorative light and a failed emergency-shutdown valve differ greatly in consequence. Criticality analysis ranks assets or failure modes according to the severity and likelihood of their consequences, while considering detectability, redundancy, and recoverability.

Typical consequence categories are:

A criticality decision should consider the whole system rather than only the purchase price of an asset. A low-cost sensor may stop an expensive line; a redundant pump may have low production consequence but high environmental consequence if its failure disables containment.

Failure consequences guide maintenance strategy. A condition-monitoring task may be justified for a critical bearing whose failure gives a detectable warning. Conversely, a simple and inexpensive noncritical component may be run to failure if replacement is safe, rapid, and economically sensible. A hidden safety function requires periodic proof testing even if it rarely operates.

4. Evolution of Maintenance Approaches

Reactive or run-to-failure maintenance

Reactive maintenance allows an asset to operate until failure, after which corrective action is taken. It is appropriate when the failure consequence is low, no useful warning exists, repair is quick, and the cost of preventive intervention exceeds the expected benefit. It is inappropriate for safety-critical, environmentally hazardous, or highly disruptive failures.

Reactive maintenance is often associated with emergency work, but planned run-to-failure is different from uncontrolled neglect. Planned run-to-failure requires an identified spare, safe isolation procedure, competent labor, and an acceptable consequence profile.

Preventive maintenance

Preventive maintenance is performed at predetermined intervals or according to prescribed criteria to reduce the probability of failure or degradation. It includes time-based inspection, lubrication, adjustment, calibration, replacement, cleaning, and scheduled overhaul.

Preventive maintenance is valuable when failure probability increases with age or usage, when deterioration is predictable, or when regulations prescribe an interval. However, fixed-interval replacement can be wasteful when age has little relationship to failure. It can also introduce maintenance-induced defects and unnecessary exposure to hazards.

A preventive replacement policy is not automatically superior to corrective maintenance. The correct question is whether the intervention reduces consequential risk or lifecycle cost sufficiently to justify its resources and disruption.

Predictive or condition-based maintenance

Predictive maintenance uses measurements or observations of actual condition to determine when intervention is needed. Common techniques include vibration analysis, infrared thermography, oil analysis, motor-current analysis, ultrasound, acoustic emission, performance trending, and online process monitoring.

The logic is condition-based: detect a measurable degradation state, estimate its significance, and intervene before functional failure. Predictive maintenance is most effective when a failure has a detectable potential-failure interval—the period between detectable degradation and functional failure—and when the monitoring method is sufficiently sensitive, specific, timely, and economically justified.

Predictive maintenance does not mean collecting data without decisions. A successful program defines measurement points, sampling frequency, alarm limits, diagnostic competence, work-order response, and escalation rules. False alarms create waste; missed alarms create risk.

Proactive maintenance

Proactive maintenance seeks to eliminate or control the causes of failure rather than repeatedly treating symptoms. It includes precision alignment and balancing, contamination control, design improvement, root-cause analysis, operator care, defect elimination, and improved installation practices.

For example, repeatedly replacing a motor bearing may be reactive or preventive. Investigating shaft misalignment, soft foot, excessive belt tension, or contamination and correcting the cause is proactive. Proactive maintenance often produces more durable gains than increasing inspection frequency alone.

Prescriptive maintenance

Prescriptive maintenance combines condition data, failure models, operational context, and decision rules to recommend or automatically initiate an action. A system may recommend reducing load, changing operating parameters, ordering a spare, or scheduling an intervention during a planned stoppage.

Prescriptive maintenance is more than prediction. Prediction estimates what may happen; prescription evaluates what should be done, considering constraints and consequences. Its value depends on trustworthy data, validated models, clear accountability, cybersecurity, and human acceptance. An algorithm should support—not obscure—engineering judgment and safety authority.

These approaches are complementary rather than mutually exclusive. One asset may use run-to-failure for a noncritical gasket, preventive calibration for a measuring instrument, vibration monitoring for a gearbox, proactive contamination control for hydraulic oil, and prescriptive scheduling for a constrained production bottleneck.

Figure: Conceptual evolution of maintenance approaches. The stages can coexist within one asset portfolio.

5. Maintenance and Business Performance

Production and delivery

Equipment availability affects effective capacity. A breakdown can stop a bottleneck, starve downstream operations, or force inefficient changeovers. Even when production continues, degraded equipment may reduce speed and create unstable flow. Maintenance planning must therefore be integrated with production planning, shutdown windows, changeovers, and spare-part availability.

Quality

Equipment condition influences dimensional accuracy, temperature control, pressure, contamination, and process repeatability. A machine that is available but produces nonconforming output is not delivering effective performance. Maintenance and quality teams should connect failure records with scrap, rework, customer complaints, and process capability.

Safety and environment

Maintenance work itself can create hazards through stored energy, confined spaces, work at height, electrical exposure, chemicals, and unexpected startup. Lockout/tagout or equivalent energy-isolation controls, permits, competency verification, and post-maintenance testing are essential. Safety devices must not be bypassed casually to improve availability.

Environmental performance is also a maintenance concern. Leaks, inefficient combustion, compressed-air losses, refrigerant escape, excessive lubrication, and abnormal energy consumption may indicate asset deterioration. Preventive and predictive tasks can reduce both environmental impact and operating cost.

Profitability and lifecycle cost

Maintenance cost is more than labor and spare parts. The total economic consequence may include lost contribution margin, overtime, expedited freight, scrap, energy waste, warranty exposure, and customer penalties. A simple lifecycle perspective is:

where LCC is lifecycle cost and each term represents acquisition, operation, maintenance, downtime consequences, and disposal costs over the chosen analysis period. The terms must be defined consistently and discounted if a formal present-value analysis is required. This is a conceptual accounting identity, not a universal cost-estimation method.

A maintenance decision should compare alternatives over the relevant horizon. A cheaper spare may have a shorter life; a more expensive sensor may prevent a major failure; a redesign may require capital but reduce recurring loss. Financial analysis should include uncertainty rather than relying only on average cost.

6. Maintenance Performance Measures

Performance indicators should support decisions, not encourage local optimization. Common indicators include:

• planned maintenance percentage;
• schedule compliance;
• emergency-work percentage;
• mean time to repair;
• mean time between failures;
• availability;
• maintenance cost per unit of output;
• repeat-failure rate;
• preventive-maintenance compliance;
• stockout frequency for critical spares;
• safety incidents and environmental releases.

For a repairable asset with n observed failure intervals T₁, T₂, …, T_n, the arithmetic estimate of mean time between failures is:

This estimate assumes comparable operating conditions and consistently recorded failure events. It should not be used blindly when the asset population is changing, failures are censored, or repairable-system data are nonstationary.

A useful availability estimate from observed operating and downtime records is:

The numerator and denominator must refer to the same asset, period, and availability definition. Planned shutdowns, waiting time, microstoppages, and quality losses should be treated consistently with the organization’s agreed standard.

Indicators can be distorted. High preventive-maintenance compliance may coexist with poor reliability if tasks are ineffective. Low maintenance cost may result from deferred work rather than efficiency. High MTBF may reflect under-reporting. Balanced measurement combines leading indicators—such as defect elimination and schedule quality—with lagging indicators such as failures and downtime.

7. Selecting a Maintenance Strategy

A defensible strategy follows a structured sequence:

1. Define the asset’s required functions and performance standards.
2. Identify credible failure modes and their causes and effects.
3. Assess safety, environmental, quality, operational, and financial consequences.
4. Determine whether degradation is detectable and whether warning time is adequate.
5. Compare run-to-failure, interval-based, condition-based, redesign, and other options.
6. Specify tasks, intervals, limits, responsible roles, tools, spares, and escalation rules.
7. Pilot and validate the strategy using evidence.
8. Review results and revise the strategy when operating context changes.

Failure Modes and Effects Analysis (FMEA) organizes this reasoning by asking how an item can fail, why it can fail, what effect follows, and how the failure can be detected or controlled. Risk-based maintenance extends the analysis by explicitly linking task selection to risk and uncertainty. Reliability-Centered Maintenance (RCM) emphasizes required functions, functional failures, failure modes, consequences, and technically feasible tasks. These methods are not substitutes for engineering knowledge; their quality depends on accurate functional definitions, realistic failure information, and cross-functional participation.

A maintenance program should also distinguish task effectiveness from task completion. A completed inspection that cannot detect the relevant failure mode has little value. Likewise, a technically sound task may be ineffective if its result does not trigger timely action.

8. Digitalization, Data, and Artificial Intelligence

Computerized maintenance management systems (CMMS) support asset registers, work orders, job plans, history, labor, costs, and materials. Enterprise asset management systems may connect maintenance with procurement, finance, production, and compliance. Sensors and industrial networks can provide high-frequency condition data.

Artificial intelligence and machine learning can assist anomaly detection, remaining-useful-life estimation, image inspection, work-order classification, and spare-parts forecasting. However, models may fail when operating regimes change, sensor data drift, failure labels are scarce, or the training data contain historical bias. A model can identify correlation without establishing physical causation.

Responsible deployment requires:

• clear business and safety use cases;
• validated sensor data and time synchronization;
• cybersecurity and access control;
• model performance monitoring;
• explainable recommendations for high-consequence decisions;
• human authority to reject unsafe recommendations;
• data retention, privacy, and ownership rules;
• documented change management and audit trails.

Technology should not be purchased as a substitute for basic maintenance discipline. Poor asset hierarchy, incomplete failure coding, weak work planning, and unreliable measurements will limit the value of advanced analytics.

9. Illustrative Case: Packaging-Line Sealing Failures

Background

A food manufacturer operates a high-speed packaging line. The line’s sealing unit contains heated jaws, pneumatic actuators, temperature sensors, and a control system. Recurrent seal defects cause product rework and occasional line stoppages.

Problem

The initial response was reactive: operators reported poor seals, technicians replaced heater cartridges, and production restarted. Monthly records showed increasing defects, repeated heater replacement, overtime, and customer complaints. The line’s nominal availability appeared acceptable, but quality losses were substantial.

Analysis

A cross-functional team defined the required function as producing sealed packages within specified temperature, pressure, and seal-integrity limits. The team identified several failure modes: heater degradation, sensor drift, jaw misalignment, contamination on the sealing surface, unstable pneumatic pressure, and incorrect setup after changeover.

The evidence showed that heater replacement addressed a symptom. Infrared measurements revealed uneven temperature distribution, while inspection found misalignment and contamination. Work orders also showed that temperature alarms were acknowledged but not investigated because alarm limits were poorly configured.

Approach and solution

The team introduced a layered strategy:

• preventive cleaning and alignment checks at changeover;
• calibration verification for temperature sensors;
• condition-based trending of temperature uniformity and pneumatic pressure;
• a standardized setup checklist;
• revised alarm limits validated against quality requirements;
• root-cause review for repeated heater failures;
• operator training in early defect recognition;
• a planned spare-heater replacement rule based on measured condition rather than arbitrary replacement alone.

Maintenance, production, quality, and engineering jointly reviewed the data each week. A safety procedure controlled access to the heated and pneumatic equipment during intervention.

Outcome

In this illustrative case, seal defects and emergency stoppages declined, while planned work increased temporarily. The most important improvement did not come from adding inspections; it came from correcting misalignment, contamination, alarm design, and setup variation. The organization also recognized that availability alone had hidden the cost of poor quality.

Learning points

1. Asset performance must be defined by required function and product requirements.
2. Repeated component replacement does not prove that the component is the root cause.
3. Predictive data are valuable only when linked to an action and a responsible owner.
4. Maintenance strategy should address production, quality, and safety together.
5. A cross-functional analysis often reveals interactions invisible within a single department.

10. Challenges, Governance, and Best Practice

Maintenance decisions involve uncertainty. Failure data may be incomplete, operating conditions may vary, and economic consequences may be difficult to quantify. Organizations must avoid false precision in risk rankings and should document assumptions, evidence quality, and decision ownership.

Governance includes compliance with applicable occupational-safety, environmental, electrical, pressure-equipment, lifting, fire, and industry-specific requirements. Legal compliance is a minimum condition, not a replacement for risk management. Contractors must receive suitable induction, supervision, access controls, and performance evaluation.

Ethically, maintenance professionals have a duty to protect people, customers, communities, and the environment. Suppressing alarms, falsifying inspection records, extending intervals without evidence, or returning unsafe equipment to service may create severe harm. Production pressure does not justify bypassing safety controls or concealing defects.

Best practice includes clean asset data, clear functional standards, risk-based prioritization, planned work, competent execution, quality assurance after maintenance, feedback from operators, effective spare-parts control, and systematic learning from failures. The maintenance system should be periodically audited for whether it controls risk and delivers value—not merely whether forms have been completed.

Chapter Summary

Maintenance management preserves and restores the functional capability of physical assets. Its essential concepts include function, functional failure, fault, defect, downtime, reliability, maintainability, availability, failure mode, failure cause, and failure consequence. Failures can affect safety, environment, quality, production, cost, and reputation; therefore, maintenance strategy should be based on consequence and risk rather than habit.

Reactive maintenance responds after failure and can be appropriate for low-consequence, easily restored items. Preventive maintenance uses predetermined intervals, predictive maintenance uses actual condition, proactive maintenance removes underlying causes, and prescriptive maintenance recommends actions by combining data, models, and constraints. These approaches can coexist across an asset portfolio.

Maintenance performance must be evaluated using clearly defined measures such as availability, MTBF, MTTR, downtime, cost, repeat failures, and quality losses. Digital systems and artificial intelligence can improve diagnosis and decision support, but they require reliable data, cybersecurity, validation, governance, and human accountability. Ultimately, effective maintenance is a cross-functional business capability linking engineering decisions with production, quality, safety, sustainability, and profitability.

Key Terms

Asset: A resource that has actual or potential value to an organization.

Availability: The extent to which an asset is capable of performing its required function when needed.

Breakdown: A failure that causes loss of function and requires corrective action.

Condition-based maintenance: Maintenance initiated according to measured or observed asset condition.

Corrective maintenance: Maintenance performed to restore a required function after a fault or failure is detected.

Criticality analysis: Evaluation of assets or failure modes according to consequence and risk.

Defect: A nonconformity or imperfection that may precede functional failure.

Downtime: Time during which an asset is unavailable or unable to perform its required function.

Failure: Termination of an item’s ability to perform a required function.

Failure mode: The manner in which an item fails.

Failure consequence: The effect of a failure on safety, environment, quality, operation, or cost.

Fault: An abnormal condition indicating actual or potential loss of function.

Functional failure: Inability to meet a specified required function or performance standard.

Maintainability: Probability of restoring an item within a specified time under stated conditions.

Maintenance: Combination of technical, administrative, and managerial actions that preserve or restore required function.

MTBF: Mean time between failures for a repairable item or system.

MTTR: Mean time to repair or restore a failed item, according to the stated definition.

Predictive maintenance: Condition-based maintenance using monitoring, analysis, and trend information.

Proactive maintenance: Maintenance focused on eliminating or controlling failure causes.

Prescriptive maintenance: Data- and model-supported maintenance that recommends or initiates an action.

Review Questions

Short Answer Questions

1. Distinguish a fault, defect, and functional failure.
2. Define reliability, maintainability, and availability.
3. What is the difference between planned and unplanned corrective maintenance?
4. Why can a low-cost component have high criticality?
5. State two limitations of fixed-interval preventive maintenance.

Long Answer Questions

1. Explain the evolution from reactive maintenance to prescriptive maintenance and identify situations in which each approach may be appropriate.
2. Discuss how maintenance affects production, quality, safety, environmental performance, and profitability.
3. Explain how FMEA or reliability-centered maintenance can support maintenance-strategy selection.
4. Critically evaluate the use of MTBF, MTTR, and availability as maintenance performance indicators.
5. Describe the governance requirements for using artificial intelligence in maintenance decisions.

Analytical / Application Questions

1. A noncritical conveyor can be repaired safely in 30 minutes, has readily available parts, and causes no quality or safety consequence when it fails. Recommend a maintenance approach and justify it.
2. A gearbox exhibits rising vibration, but the production line is scheduled to run continuously for two weeks. Develop a decision process using condition data, risk, production requirements, and a planned intervention window.
3. A plant reports high preventive-maintenance compliance but rising emergency work and repeated failures. Diagnose possible causes and propose a balanced improvement plan.

Multiple Choice Questions with Answers

Question 1

Which statement best defines functional failure?

A. Any visible deterioration of an asset

B. Inability to perform a required function to the specified standard

C. The completion of a maintenance work order

D. A reduction in spare-parts inventory

Correct Answer: B. Inability to perform a required function to the specified standard

Question 2

Which approach uses measured asset condition to determine when maintenance is needed?

A. Reactive maintenance

B. Fixed-interval replacement

C. Predictive maintenance

D. Run-to-failure without planning

Correct Answer: C. Predictive maintenance

Question 3

A maintenance-induced failure may result from:

A. Correct alignment and contamination control

B. Incorrect assembly or wrong lubrication

C. Accurate functional testing

D. Validated alarm limits

Correct Answer: B. Incorrect assembly or wrong lubrication

Question 4

In the expression A_i = MTBF ÷ (MTBF + MTTR), what does MTTR represent?

A. Mean time to repair

B. Maximum time to production

C. Mean temperature trend ratio

D. Maintenance task risk

Correct Answer: A. Mean time to repair

Question 5

Which situation most strongly supports planned run-to-failure maintenance?

A. Failure could cause an explosion

B. Failure disables a legally required safety function

C. Failure is low consequence and can be restored quickly and safely

D. Failure causes major customer contamination

Correct Answer: C. Failure is low consequence and can be restored quickly and safely

Question 6

What is the principal focus of proactive maintenance?

A. Increasing emergency overtime

B. Eliminating or controlling underlying failure causes

C. Replacing every component at the same interval

D. Ignoring condition-monitoring results

Correct Answer: B. Eliminating or controlling underlying failure causes

Question 7

Why should availability indicators be carefully defined?

A. Availability never includes time measurements

B. Different definitions may include or exclude planned work and delays

C. Availability applies only to nonrepairable items

D. Availability is identical to product quality

Correct Answer: B. Different definitions may include or exclude planned work and delays

Question 8

Which is an example of a failure consequence rather than a failure mode?

A. Bearing seizure

B. Sensor drift

C. Seal leakage

D. Loss of production capacity

Correct Answer: D. Loss of production capacity

Question 9

What is a key requirement for responsible maintenance AI?

A. Automatic action without validation

B. Reliable data, cybersecurity, and human accountability

C. Elimination of engineering judgment

D. Use of the largest possible data set regardless of quality

Correct Answer: B. Reliable data, cybersecurity, and human accountability

Question 10

A machine operates for 90 hours and is unavailable for 10 hours during a defined observation period. Using operating time divided by operating time plus downtime, its availability is:

A. 10%

B. 80%

C. 90%

D. 100%

Correct Answer: C. 90%