Maintenance is generally performed on machinery at one of two times: when something’s already gone wrong, or according to a predetermined, regular schedule. While one hopes that the regularity of scheduled maintenance reduces the unplanned downtime associated with an outage, companies basically roll the dice to prevent these issues. If the outage occurs between regularly scheduled measurements, it is mostly bad luck. But what if there was a more proactive approach…one that accounts for the current condition of the machine itself.
Enter condition-based monitoring (CBM). The CBM approach performs maintenance on a machine only when its condition demands it. Correctly executed, condition-based maintenance is administered before failure and downtime, by using real-time and predictive data about a machine’s status and performance.
Condition-based maintenance is often confused with condition-based monitoring. While they complement one another, they are distinctive. While condition-based maintenance delivers service based on condition status, condition-based monitoring is the mechanism for constant visibility into condition status. In other words, condition-based maintenance depends on condition-based monitoring. This monitoring is facilitated through IIoT-connected sensors that can read a variety of factors, including acoustic, infrared, ultrasound, pressure, RPMs, etc.
Traditionally, measurements were taken with handheld sensors at regular intervals—most often weekly. However, there are two major problems with traditional, scheduled maintenance:
Both of these can increase maintenance expenses. The former, when machines fail unexpectedly, causes unscheduled downtime—and requires more costly repairs than if the issue had been identified earlier. The latter, when technicians are called out to service a machine in perfect working order. Condition based maintenance aims to solve both problems.
By monitoring machine health on a consistent basis, it becomes possible to identify degradation before it evolves into a larger problem. Remedying the smaller fault can prevent unscheduled downtime, cost less, and extend the life of the machine. And by only servicing when such an aberration has been detected, maintenance expenses are only incurred when they’re actually necessary. Ultimately, the result is greater operational efficiency, lower maintenance costs, and a better ROI on your machinery.
People often think of ‘condition-based maintenance’ as ‘predictive maintenance,’ and in fact, the two terms are sometimes used interchangeably. However, although there is substantial overlap, there is a significant difference. Both are forms of proactive maintenance, aiming to anticipate and prevent machine faults. But predictive maintenance uses the constant stream of data available from IIoT sensors to greater effect. It employs big data methodology to predict degradation based on previous history and related datasets, rather than simply on their current condition. The principle benefits are that predictive maintenance can catch potential issues even earlier.
Automatic, constant, and real-time data streams using permanently-installed industrial Internet of Things (IIoT) sensors allow technicians to extend the time they have between identifying a potential fault and a functional fault—known as the P-F interval, allowing preventative servicing to be scheduled more efficiently.
Condition based maintenance always runs the risk that multiple machines will need servicing at the same time. With predictive maintenance’s larger window between potential and functional fault, there’s more room to space the work out.
The downside of predictive maintenance is cost. To properly function, it requires vast arrays of sensors producing equally vast amounts of data, knowledge of how to mine the resulting data lakes, and the IT infrastructure to process it. For predictive maintenance advocates, the cost is outweighed by extended machine life and lower long-term maintenance costs. However, some manufacturers may find condition-based maintenance’s lower, short-term expenses to be more beneficial—even if it ends up costing more over time.
The specifics of a condition-based maintenance implementation will vary enormously between individual manufacturers. However, these are the essential steps:
Technologically, condition-based maintenance is well established, and your solutions provider can help guide you through the necessary steps for implementing the sensors and software. The biggest challenge in implementation is ensuring your staff have the right skills for the new workflow. Digital solutions like AR and the IIoT are already helping companies like Huawei and Mitsubishi to close skills gaps like these, and help workers carry out tasks beyond their skillsets using AR instruction overlays and remote assistance.
To find out more about how digital solutions like AR and the IIoT can play a key role in implementing—and augmenting—your condition-based maintenance program, download our research report Augmented Reality for Maintenance, Repair and Overhaul (MRO). And, learn more about PTC’s Remote Condition Monitoring and Intelligent Asset Optimization Solutions.