Predictive maintenance helps to predict when maintenance is needed to be undertaken. It makes use of data science and predictive analytics to predict when an equipment or machinery must be scheduled for maintenance before the equipment reaches a point of failure. Predictive maintenance ensures that maintenance works are scheduled in a time that is considered as most convenient and cost-efficient. It also aims to ensure that the lifespan of the equipment is most effectively used. Simply stated, predictive maintenance minimizes the likelihood of the breakdown of an equipment.
Corrective Maintenance is often implemented when a fault is identified on an equipment. It ensures that the equipment works again and can work the way it is expected. Corrective maintenance can be planned or unplanned and this is contingent on whether a maintenance plan has been created or not. When failure is not anticipated, technicians apply the unplanned corrective maintenance. Unplanned corrective maintenance can be overly expensive as compared to the planned corrective maintenance. This is because the unplanned corrective maintenance leads to unbudgeted costs.
It seeks to cut down on the possibility of the breakdown of an equipment and helps to prevent such occurrences from happening. Teams normally consider the history and past failures of the equipment and ensure that this does not happen again. In doing this, they can identify the time within which a likely breakdown might occur and restrain such similar occurrences. This type of maintenance can be described as planned because it puts in place well-established maintenance programs. The preventive maintenance can be applied because of the existence of a computerized maintenance management system (CMMS). CMMS is an essential tool that any company can use to organize its maintenance department and therefore ensures long-lasting productivity.
Risk-based maintenance (RBM) seeks to handle risk-sensitive systems and machinery. It identifies the most cost-effective way to distribute resources to minimize or repair risk. RBM aims to secure employees, employers, and the assets of a company. It offers well-tested and low-cost processes to protect the most exposed part of an equipment or system. Having said this, risk-based maintenance has a positive impact on a company especially in terms of its cost-effectiveness.
This is a type of predictive maintenance that relies on sensor data, such as vibration monitoring systems. It measures the condition of an equipment over time while it is in operation. Maintenance is only performed when the dataset for predictive maintenance indicates that performance has decreased. Even though Condition-based maintenance is the most complicated amongst the other types mentioned, it acts as a measure that prevents failures from occurring. With CBM, regular check-ups of the state are needed. Just like the RBM, the CBM is highly cost-effective and allows companies to save money.
This may be the least popular predictive maintenance amongst all. It focuses on the programs delivered by the manufacturers and not on the state of the actual equipment. They focus on the program based on their knowledge of failure mechanisms as well as MTTF (mean time to failure) as they have witnessed in the past. Either the equipment is old or new, the failure risks of this maintenance is either higher or lower based on the assumption that this type of maintenance is only applied according to the elaborations of the program made by manufacturers. Just like the other types, the predetermined maintenance is not perfect and there is no guarantee that a piece of equipment will not break down.
1. Power outage prevention
2. Building management
3. Oil and Gas Industry
4. Refrigeration sensor
5. Aircraft maintenance
Predictive maintenance uses historical and real-time data from various parts of your operation to anticipate likely future problems.
It is essential to analyze the data that the machine has already produced. This can be done by collecting and analyzing a range of key data sets from most industrial machines that many manufacturers are using. Data relating to the machine current, torque or pressure can be all that is needed to detect initial signs of problems.
A key factor in scaling predictive maintenance is to ensure that the algorithms you start with can be applied to any machine from any manufacturer. Instead of creating particular algorithms for each machine, it is recommended that you start with common algorithms and allow them to improve themselves over time.
Scaling up predictive maintenance initiatives using traditional approaches would involve employing more data scientists or retraining engineers with a completely new set of skills. Advances in A.I. mean that it is now possible to collect and analyze machine data at an enormous scale. Computers can crunch through data from tens of thousands of machines automatically to spot the signs that a machine is failing. Large amounts of computer resources are required to facilitate the number crunching this kind of large-scale condition monitoring requires. The cloud provides that power precisely in line with demand. Initiatives proven on a small cluster of machines can be expanded rapidly to many thousands more.
Predictive maintenance is extremely important in improving the overall maintenance of an equipment or operation. Some of the benefits of predictive maintenance includes the following:
1. It improves safety
2. It can also save lives. Predictive maintenance can be used in various industries including drone technology. Drones can be used to map power lines and networks. Machine learning analyses uses the images and recognises trees that are in danger of falling on the lines. <H6> 3. Through predictive maintenance, such trees can be removed or trimmed to reduce the risk.
4. It maximizes production hours
5. It also minimizes unexpected breakdowns
6. It streamlines maintenance costs through reduced equipment, inventory costs, and labor.
1. There can be misinterpretation of data which may lead to false maintenance requests.
2. Predictive maintenance may negatively have an effect on physical inspection and the maintenance of equipment.
3. It is expensive to establish an entire IoT system with analysis, transmission costs and analysis.
4. Predictive maintenance may leave out relevant contextual information such as the weather and how long an equipment has been in usage.
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