Contents

Condition Based MaintenanceEdit

Condition-Based Maintenance (CBM) is an asset-management approach that uses real-time or near-real-time information about the condition of equipment to determine when maintenance should be performed. Rather than servicing components on a fixed calendar interval, CBM aims to act when indicators show that a part is deteriorating or nearing failure. This approach relies on sensors, data acquisition, and analytics to monitor indicators such as vibration, temperature, oil quality, wear debris, and performance metrics.

CBM is a practical realization of reliability-centric thinking in asset management. It sits alongside related concepts such as predictive maintenance and Reliability-Centered Maintenance (RCM) within the broader domain of Asset management and maintenance, repair and overhaul (MRO). By aligning maintenance with actual equipment health, CBM seeks to maximize uptime, extend asset life, and reduce total maintenance costs. It has found traction in industries where downtime is costly and safety-critical components demand high reliability, including manufacturing, energy, transportation, and aerospace sectors, often enabled by Industrial IoT platforms and data analytics.

Overview

  • Definition: CBM bases maintenance actions on observed or predicted equipment condition rather than fixed dates or usage thresholds.
  • Core data sources: sensors measuring vibration, temperature, pressure, acoustics, oil condition, and other performance indicators; plus diagnostic tests and run-time data from equipment control systems.
  • Decision logic: can combine rule-based alarms with trend analysis and prognostics to determine the optimal time for intervention.
  • Relationship to other approaches: CBM is closely related to, and sometimes overlaps with, predictive maintenance; it is part of the broader discipline of Reliability engineering and sits within Asset management strategies.
  • Typical outcomes: reduced unplanned downtime, better spare-parts planning, improved safety, and lower lifecycle costs for assets.

Concepts and Techniques

  • Condition monitoring and sensing: CBM depends on reliable sensors and monitoring systems. Common indicators include vibration analysis to detect bearing wear or misalignment, thermography to identify overheating, and oil analysis to monitor lubricant condition and wear particles. Other indicators include electrical signatures, hydraulic pressures, and performance efficiency.
  • Data collection and integration: Data streams from sensors are aggregated into a centralized or cloud-based repository, often within a CMMS (Computerized Maintenance Management System) or an EAM (Enterprise Asset Management) platform, enabling trend tracking and diagnostics.
  • Prognostics and health assessment: Beyond simple thresholds, CBM increasingly uses prognostic methods to estimate remaining useful life of components, guiding maintenance timing before failure occurs.
  • Decision rules and automation: Maintenance actions may be triggered by alarms, predictive scores, or automated maintenance orders, integrating with workflows in enterprise resource planning (ERP) and maintenance platforms.
  • Digital twins and simulation: In advanced programs, a digital twin of a component or system may be used to simulate responses to wear and operating conditions, refining maintenance plans.
  • Standards and governance: Frameworks such as ISO 13374 provide guidance on data collection, diagnostics, and prognostics for condition monitoring, while governance practices address data quality, cybersecurity, and compliance.

Implementation and Benefits

  • Asset health and uptime: CBM can improve asset availability by addressing faults before they become catastrophic, reducing unplanned downtime.
  • Lifecycle cost optimization: By avoiding unnecessary service and aligning interventions with actual wear, CBM can lower total cost of ownership for assets.
  • Spare-parts and maintenance planning: More accurate forecasts of when parts will be needed help optimize inventory and labor scheduling.
  • Safety and regulatory compliance: When tied to risk-based maintenance practices, CBM supports safety-critical assets by maintaining performance within specified limits.
  • Competitive considerations: Companies that adopt CBM alongside other digitalization efforts can gain efficiency, though benefits depend on data quality, sensor coverage, and integration with maintenance workflows.

Risks, Challenges, and Debate

  • Upfront investment and ROI uncertainty: Implementing CBM requires sensing, data processing, analytics capabilities, and integration with existing systems, which can involve substantial upfront costs and a cautious payoff curve.
  • Data quality and integration: The effectiveness of CBM hinges on reliable data; poor sensor performance or gaps in data streams can lead to incorrect maintenance decisions.
  • Complexity and organizational alignment: Successful CBM often demands changes in maintenance culture, skilled personnel, and cross-functional coordination between operations and maintenance teams.
  • Overreliance on data: There is a risk of overfitting maintenance decisions to imperfect models or misleading indicators, especially in assets with complex failure modes.
  • Security and privacy: As CBM relies on connected devices and data networks, cybersecurity and data governance become important considerations for protecting asset information.
  • Standards and interoperability: Varied hardware, software, and vendor ecosystems can complicate data integration and consistency of practice across sites.

See also