The industry 4.0 has revolutionized manufacturing by giving companies the opportunity to deploy machine-to-machine (M2M) and machine-to-human (M2H) communication together with analytical technologies so that breakdowns can be predicted always on time. In the context of the latest digital trends, predictive algorithms supported by next-gen CMMSs have recently become more refined and robust. Proactive maintenance strategies have enormously eased the management and maintenance of company’s assets by optimizing workforce schedules and prolonging equipment life.
How Does Predictive Maintenance Work?
The core idea of predictive maintenance is to predict when equipment failure might occur based on condition-monitoring data. Furthermore, predictive maintenance embraces the performance of regular maintenance activities but with as low as possible frequency in order to prevent the occurrence of breakdowns.
PdM algorithms are based on the data provided by diverse condition-monitoring tools as vibration and oil analysis. Comparing the expected asset’s behavior and its gradual deterioration identifies trends and determines when a piece of equipment needs a special attention.
Moreover, predictive maintenance monitors the condition and performance of equipment during normal operation and thus reduces disruption of everyday operations.
The Condition-Monitoring Tools
Choosing the best condition-monitoring technique depends on the company’s needs and the type of assets an organization employs. The chosen tool should be highly effective, but also provide sufficient warning time for upcoming maintenance.
This is a short overview of the most common condition-monitoring tools used in predictive maintenance:
- Vibration analyses are mainly used to detect misalignment, imbalance, mechanical looseness or wear on pumps or motors.
- Infrared thermography identifies temperature fluctuations in transmissions, gearboxes, bearings and many more with infrared cameras.
- Oil analysis determines, by measuring an asset’s number and size of particles, a lubricant’s health and if it has been contaminated.
- Ultrasound analyses are used to detect leak in pipe systems, tanks; mechanical malfunctions of movable parts and faults in electrical equipment.
- Current analysis measures the current and voltage of electricity supplied to an electric motor.
Some other known condition-monitoring techniques include shock pulse, fluid analysis, performance trending, stereoscopic photography and material (non-destructive) testing, e.g., ultrasonic, eddy current, borescopic inspections.
Predictive Maintenance and Mobility Work
You can accept predictive maintenance as a useful feature of your CMMS or your CMMS as a core component of your predictive maintenance program. Whatever you choose, one is clear: Predictive maintenance and a CMMS offer plenty of undoubted advantages when combined.
By storing and analyzing condition-monitoring data, Mobility Work allows users to define boundaries of acceptable values for assets and auto-generate work orders or notifications when the readings fall outside of the predefined values.
Furthermore, Mobility Work analyzes all the data in the context of previous maintenance interventions and reports and spare part information to produce most reliable trends and contribute to the asset life cycle analysis. Based on this the CMMS can predict when an asset will require maintenance or a replacement.
For more details on how to combine condition-monitoring data and CMMS, read our blog article: Predictive Maintenance and CMMS: Get the Best Out of It
How to Implement a Predictive Maintenance Program?
First of all before implementing predictive maintenance, you have to understand what the basic technology does and its relationship to the types of equipment it has been applied against. Once you get the basics of PdM in place, your strategy will be planned. The next important step is to choose the most effective condition-monitoring technique for you and to acquire the needed machinery as sensors. Then, you have to integrate the condition-monitoring data into your CMMS. It is of crucial importance that all work that is performed should be identified and input to a CMMS, otherwise it can’t be tracked and if it can’t be tracked, it is lost.
If you have followed all the mentioned basic steps, this would allow you to minimize all reactive work and rather plan and schedule every single maintenance activity. This will help you to adjust resources accordingly and reduce costs by simple work management activities.
You have to be prepared that the implementation of the maintenance strategy will be accompanied by some occasional problems related to various reasons. Plan your time accordingly and give your team the ability to discuss these problems in order to avoid them in the future.
And last but not least, in order to understand how beneficial predictive maintenance is to your organization, you should measure a few previously identified specific KPIs.
When Predictive Maintenance Doesn’t Make Any Sense?
The following factors should be considered when identifying which assets should be included in the predictive maintenance program. The assets that are suitable for predictive maintenance have usually a critical operational function and allow the performance of regular monitoring to cost-effectively detect failure modes.
Advantages and Disadvantages of Predictive Maintenance
The upfront cost for establishing predictive maintenance is quite high because of the required condition monitoring equipment and the highly experienced workforce needed to accurately interpret all the data. However, the advantages that a PdM can deliver to an organization outweigh by far the cost considerations. If a predictive maintenance program has been properly established and is effectively working as a maintenance strategy, it brings the following potential cost savings:
- Minimized time for equipment maintenance; maintenance is performed only when an asset requires it
- Minimized production hours lost due to maintenance, since PdM is performed while equipment is running
- Minimized spare parts cost.
Besides these ones, a successful predictive maintenance strategy can entirely reshape in a positive way a company as a whole. Reliability should be considered as the core concept of PdM embracing downtime reduction and productivity increase by ensuring a piece of equipment remains operating.
Predictive maintenance is about to become the next must maintenance strategy for successful businesses. Its long-term benefits result in significant improvements in asset reliability and a boost in cost efficiency. Driven by a next-gen CMMS as Mobility Work, predictive maintenance can easily become your most powerful maintenance tool.