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Unlocking the Accuracy of Condition-Based Maintenance

July 23,2025

Introduction
Among equipment maintenance strategies, preventive maintenance relies on analyzing historical operating records to schedule fixed inspection cycles, while predictive maintenance leverages real-time equipment health data to determine when maintenance or component replacement is necessary. Condition-Based Maintenance (CBM) builds on this principle, leveraging continuous monitoring data to schedule inspections at the optimal time, thereby avoiding premature or delayed maintenance.

Recent advances in sensor technology, data acquisition devices, and industrial networks have made it easier to collect operating parameters from individual equipment in real time. By comparing this real-time information with historical performance curves and theoretical operating indicators, it is possible to accurately determine whether the equipment is stable or approaching its performance limits. This capability enables the factory's operations and maintenance team to intervene before a failure occurs and schedule repairs at a time that does not impact production schedules and is convenient for maintenance personnel.

A significant advantage of this strategy is that it avoids frequent and unnecessary equipment downtime. In contrast, scheduled maintenance can sometimes lead to over-maintenance—a practice that not only fails to improve equipment performance but can even be counterproductive. For example, forcibly re-lubricating a well-lubricated bearing may reduce its efficiency and even introduce contaminants, accelerating component wear.

Identifying the Optimal Timing of Maintenance
Condition-based maintenance is essentially a model for equipment health management centered on real-time monitoring. When the system detects even the slightest degradation in performance, it issues an alert, giving operations and maintenance personnel ample time to prepare for repairs and allowing them to flexibly schedule production line downtime.

Repair costs typically rise rapidly as the severity of equipment failure increases. Taking action at the earliest possible moment can restore equipment to normal operation at the lowest cost. Conversely, waiting until a failure is nearing its critical point not only increases parts and labor costs but also potentially leads to extended downtime.

Therefore, identifying the "optimal maintenance point" is crucial. Condition-based maintenance can help factories avoid redundant tasks associated with traditional scheduled maintenance, reducing the additional costs associated with unnecessary maintenance and preventing production losses caused by delays.

Earlier detection of potential failures also yields a series of cascading benefits: extended equipment life, improved production safety, stable product quality, and enhanced customer satisfaction. McKinsey analyzed a case study of a large technology manufacturing company that established a CBM framework, integrating multi-dimensional data from Industrial Internet of Things (IIoT) terminals, sensor systems, and historical service databases. Ultimately, the company achieved approximately 30% savings in labor, spare parts, downtime, and other costs.

The Practical Implementation and Benefits of CBM
Many international technology and industrial giants have proven the value of condition-based maintenance. For example, when IBM launched its Maximo intelligent asset management platform, based on years of research and data analysis, they identified five core benefits of CBM that are universally applicable in the manufacturing industry:

Preventing equipment failures and unplanned downtime—Performing maintenance before problems escalate.
Extending the life of critical assets—Reducing unnecessary part replacements and wear.
Improving production and personnel safety—Reducing safety risks through more sensitive detection technologies.
Optimizing maintenance costs—Avoiding blind scheduled maintenance and focusing resources on solving real problems.
Increasing work efficiency—Allowing engineering teams to focus on critical equipment requiring intervention.

In terms of practical results, IBM data indicates that combining CBM with predictive maintenance can help companies reduce maintenance costs by 15% to 20% and increase equipment availability by approximately 20%. These savings are not only economical but also represent a comprehensive improvement in production capacity stability and competitiveness.

Conclusion
Condition-based maintenance has become a key trend in industrial maintenance systems. It breaks the limitations of the traditional "time-driven" maintenance model and deeply aligns maintenance plans with the actual condition of equipment. For manufacturers striving for high utilization rates, low operating costs, and continuous quality control, CBM is not only a cost optimization tool but also a strategic means to enhance operational resilience and equipment reliability.

With the continued maturity of artificial intelligence analysis, IIoT platforms, and advanced sensing technologies, CBM will further integrate with predictive maintenance to form a more intelligent, efficient, and cost-effective asset management solution. Companies that pioneer CBM systems will seize the initiative in future competition.

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