Condition-based maintenance (CBM) relies on sensing equipment (online or offline) to monitor parameters that relate to the physical condition of machines.

These parameters can come from a variety of sources (examples include vibration, current, thermographic or acoustic sensing), and the data captured can be interpreted by highly trained experts to detect and diagnose machine degradation, allowing them to identify when and where to perform maintenance based on current machine condition.  

When would you use condition-based maintenance?

For organizations looking for a more accurate way of scheduling maintenance based on real-time machine condition, CBM allows you to monitor and identify machine degradation to enable proactive scheduling of maintenance activity when it is needed. CBM can result in a more effective maintenance strategy when compared with time or calendar based preventative maintenance techniques. Traditionally it has been most often found in larger organizations with critical equipment to monitor and significant downtime or safety issues to mitigate. However, advances in technology associated with Industry 4.0 has made CBM available to the much wider manufacturing community, as an affordable and scalable solution for maintenance.

Types of condition-based maintenance

CBM is a broad category of maintenance that uses real-world data collected through condition monitoring, exactly how it is applied will depend on the methods used to check machine performance and degradation. These methods can be categorized in two ways although there may sometimes be a mixture employed:

CBM based on offline condition data

Uses data collected ‘by hand’, typically on a time-based interval using a handheld data acquisition system or other non-real-time methods such as oil sample analysis.

CBM based on online condition data

Uses data collected by continuous monitoring systems to provide a real-time feed of data that can be interpreted by maintenance professionals much quicker than offline data.

Important considerations with condition-based maintenance

Condition based maintenance relies on understanding complex data, often requiring a high degree of human time and effort to properly understand and subsequently program in the best maintenance actions. When the resources for this are available, it can be extremely effective in allowing damaged components to be spotted and replaced before they result in unplanned machine downtime or quality and production issues further down the line.

Advantages of condition-based maintenance


The ability to reliably and frequently collect data while the machine is still running reduces the need to disrupt operation for inspections and provides more representative data as it is captured during regular operation. 

Based on actual machine condition 

As opposed to preventative maintenance, condition-based maintenance is always based on the real-world (current) state of machinery and components. This helps problems to be mitigated before they cause failure. 

Increased maintenance efficiency

As maintenance can be performed when it is required, parts with plenty of service-life left are not needlessly replaced. This also means fewer spare parts inventory is needed and maintenance should be quicker and more precise.

Disadvantages of condition-based maintenance

Significant investment 

Although this is changing with wireless sensing systems, depending upon the underlying technology there can be a significant upfront cost to purchase and install equipment to capture condition data, as well as planned downtime required to gain access to machines.  

High effort

Interpreting data from condition monitoring systems requires a high degree of training. This does not scale well beyond tens of critical assets and becomes expensive if scaling is dependent on skilled human resources. 

Limited to current condition 

CBM uses data that represents the current condition of machinery. When degradation is detected, a level of damage has already occurred. As CBM has no ‘forward looking’ predictions, there can be instances where damage is already in a significant state and due to other demands on maintainers it is not uncommon for systems to go overlooked until machine failure occurs.

technician diagnostics

Making condition-based maintenance more efficient

CBM is a more effective and efficient approach than preventative maintenance but in most industries has seen limited adoption for all but critical machines due to the high effort overhead required to identify early signs of failure. The underlying sensing technology to support it (especially online condition monitoring) is an excellent basis for a more efficient and advanced maintenance methodology to automate the analysis of condition monitoring data.

Predictive maintenance uses Artificial Intelligence (AI) and advanced algorithms to automatically monitor current and future state of machinery, removing the significant cost of human analysis from CBM. This enables truly scalable, and sustainable predictive maintenance without the need for expensive consultants or extensive training. 

Whether you’re just starting or you’re ready to take condition-based maintenance to the next level, predictive maintenance is the most sustainable solution for any organization looking to improve its maintenance efficiency and reduce costs.

Find out more

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