The New Standard in Manufacturing Maintenance

Condition-Based Monitoring: The New Standard in Manufacturing Maintenance


Condition-Based Monitoring (CBM) is a proactive maintenance strategy that involves continuous or periodic monitoring of machinery and equipment to assess their actual condition in real-time. This approach utilises data-driven insights to determine the precise moment when maintenance should be performed, optimising maintenance schedules and minimising downtime.

Historically, maintenance strategies in manufacturing have evolved significantly. Initially, reactive maintenance, or “run-to-failure,” was common. This approach only addressed issues after they occurred, often leading to significant downtime and high repair costs. Preventive maintenance later emerged, scheduling regular maintenance tasks regardless of equipment condition, which, while reducing unexpected failures, often resulted in unnecessary maintenance activities and inefficiencies. Today, CBM and predictive maintenance represent the forefront of maintenance strategies, using advanced technologies to monitor equipment in real-time and predict failures before they happen .

How Condition-Based Monitoring Works

Condition-Based Monitoring Techniques and Technologies

CBM employs various techniques to monitor equipment conditions, including vibration analysis, infrared thermography, ultrasonic testing, and oil analysis. These methods help detect potential issues early, such as imbalances, overheating, structural flaws, and contamination, allowing for timely interventions that prevent major breakdowns and extend equipment lifespan.

Examples of Sensors and Data Collection Methods

CBM relies heavily on sophisticated sensors and data collection methods. For instance:

  • Vibration Sensors: Used to measure vibration levels and detect imbalances or misalignments in rotating machinery.
  • Temperature Sensors: Monitor temperature variations to identify overheating components.
  • Pressure Sensors: Measure pressure levels to detect leaks or blockages.
  • Ultrasonic Sensors: Detect high-frequency sound waves emitted by leaks or cracks.
  • Oil Quality Sensors: Analyse lubricant quality by detecting contamination and wear particles.
  • Humidity Sensors: Measure moisture levels to prevent corrosion and detect leaks.

Data from these sensors are collected through various methods, including wireless sensor networks (WSNs), edge computing, cloud computing, and mobile data collection. These methods enable real-time monitoring, reduce latency, and allow for scalable storage and advanced analytics capabilities.

Predictive Analytics and Machine Learning in Condition-Based Monitoring

Predictive analytics and machine learning (ML) are integral to modern CBM systems. Predictive analytics involves using statistical techniques and algorithms to analyse historical and real-time data, forecasting equipment failures and maintenance needs. Machine learning enhances this process by continuously learning from data to improve the accuracy of predictions and automate insights.

These technologies help manufacturing companies reduce downtime by predicting potential equipment failures, optimise maintenance schedules based on actual equipment conditions, and improve overall operational efficiency.

Benefits of Condition-Based Monitoring Over Traditional Maintenance

Comparison with Reactive and Preventive Maintenance

CBM offers significant advantages over traditional maintenance strategies such as reactive and preventive maintenance:

  • Cost Savings: By addressing issues before they lead to failures, CBM reduces repair costs and extends equipment life. Preventive maintenance, while reducing unexpected failures, often results in over-maintenance and higher long-term costs due to unnecessary interventions.
  • Efficiency: CBM ensures maintenance activities are only performed when necessary, based on real-time data, optimising resource utilisation and operational efficiency. In contrast, preventive maintenance schedules maintenance at fixed intervals, which may not align with the actual condition of the equipment.
  • Downtime Reduction: CBM minimises downtime by detecting potential issues early and scheduling maintenance during non-critical times. Preventive maintenance, while reducing the likelihood of unexpected failures, still includes scheduled downtime that may not always be necessary.

Industries and Applications

Impact of CBM in Various Industries

Condition-Based Monitoring is making a substantial impact across various industries, including manufacturing, OEM equipment manufacturing, aggregate and mining, recycling, and healthcare. For example:

  • Manufacturing: CBM helps reduce downtime, save costs, increase equipment lifespan, enhance product quality, and improve safety by monitoring the real-time condition of machinery.
  • OEM Equipment Manufacturers: CBM provides valuable data for improving product design and performance, offering better customer support through predictive maintenances.
  • Aggregate and Mining: CBM monitors the health of critical equipment such as crushers and conveyors, preventing costly failures and ensuring smooth operations.
  • Recycling: CBM ensures recycling machinery operates efficiently, reducing maintenance costs and extending equipment life.
  • Healthcare: CBM ensures the reliability of critical medical equipment, improving patient care and reducing costs associated with emergency repairs.

Top 5 Specific Applications in Manufacturing

Specific applications of CBM within manufacturing include:

  1. Rotating Machinery Monitoring: Detects imbalances and bearing wear early, reducing emergency repairs and improving efficiency.
  2. CNC Machine Tool Monitoring: Ensures precision and efficiency, preventing costly damage and reducing scrap rates.
  3. Conveyor System Monitoring: Maintains continuous material flow, avoiding bottlenecks and unexpected stoppages.
  4. HVAC System Monitoring: Ensures efficient operation, reducing energy consumption and maintenance costs.
  5. Power Supply and Electrical Systems Monitoring: Prevents costly electrical failures, ensuring stable power supply and minimising downtime.


Adopting Condition-Based Monitoring (CBM) is crucial for manufacturing leaders aiming to enhance operational efficiency, reduce costs, and improve equipment reliability. CBM’s data-driven approach allows for optimised maintenance schedules, minimising downtime and extending equipment lifespan. The next blog in this series will explore the economic value of CBM, providing insights into how it can drive substantial financial benefits for manufacturing companies.

About AssetMinder Predict

Condition-Based Monitoring (CBM) is revolutionising the manufacturing industry, and AssetMinder Predict is your key to unlocking its full potential. With advanced sensors, real-time data analytics, and machine learning, AssetMinder Predict offers proactive maintenance, enhanced efficiency, cost savings, and improved safety. It continuously monitors your machinery, providing early warnings and actionable insights to prevent failures before they occur, optimise maintenance schedules, and reduce unnecessary interventions.

Ready to transform your maintenance strategy? Visit AssetMinder Predict to see how this innovative solution can revolutionise your operations and arrange a demo today!

Don’t wait for the next failure—predict and prevent it with AssetMinder Predict. Embrace the future of maintenance and discover unparalleled reliability and efficiency today.

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