Predictive Maintenance

For modern industrial operations, predictive maintenance is a critical practice. It essentially involves the use of artificial intelligence and machine learning technology to anticipate when maintenance is required on a machine or system, thereby preventing unnecessary downtime and reducing maintenance costs. For bigger organizations that have outgrown traditional maintenance programs and are looking for something more effective, predictive maintenance can result in significant cost savings. 

What is Predictive Maintenance?

Predictive Maintenance is a condition-based maintenance technique that can help determine the condition of machines during regular operations. It makes use of sensor devices that supply the maintenance program with data in real time, which is then used and analyzed to predict when equipment maintenance is required to prevent unexpected failures. 

This approach differs from preventive or reactive maintenance because maintenance tasks are performed only when called for. Preventive maintenance or time-based maintenance runs the risk of either too much or too little maintenance. In the case of reactive maintenance, maintenance is performed when the machine breaks down, but it results in costly downtimes.

Due to the convenience and effectiveness of predictive maintenance, manufacturers across the board prefer it to other maintenance techniques. 

History of predictive maintenance

Organizations first started using predictive maintenance tools in the early 2000s. To monitor the condition of the machines, an offline approach was used which required periodic measurement of the equipment’s condition and comparing these results with those gathered before. But today, an online approach is put in place which constantly monitors and studies the condition of the machine and triggers in case of an anomaly. 

Why is predictive maintenance important?

Implementation of predictive maintenance promotes the longevity of critical assets and ensures that systems operate for as long as possible. One of the primary benefits it has to offer is that it can detect when potential problems might turn into serious and irreversible issues. By monitoring the performance of the machines in real-time, predictive maintenance can identify patterns that indicate potential problems, and maintenance can be scheduled accordingly. 

The differentiating factor for any organization can be the application of predictive maintenance for the below-listed reasons – 

1. Decreased Downtime – Because predictive maintenance allows technicians to detect and work on the problem in advance, downtime can be reduced by 30%. When one crucial piece of equipment fails, it leads to the production line shutting down and with predictive maintenance, this problem can be evaded. Thus, the productivity of the entire operation will be increased, and the opportunity to save costs lost to unexpected downtimes will be saved. 
2. Increased worker productivity – You can schedule maintenance beforehand with predictive maintenance and this allows scheduling to be done around the worker’s schedules. If there is some disruption in machinery, the workers’ productivity is not disrupted. The overall asset utilization is also increased due to predictive maintenance.
3. Reduced Costs – Predictive maintenance is beneficial for organizations in saving costs across multiple fronts. Early detection of the problem in machinery lets maintenance be performed when it is less expensive rather than costly repairs after the failure occurs. Additionally, unnecessary maintenance work can also be avoided with this technique because only those parts are replaced or repaired that require it. 
4. Improved Safety – Equipment failure can cause injuries, property damage and even catastrophic accidents. Such occurrences can be minimized by constant monitoring of machines through predictive maintenance. When any deviations in normal functioning are detected, the concerned authorities are informed, and thus maintenance teams can intervene at the appropriate time.
5. Increased Lifespan of Machinery – When any equipment undergoes constant supervision, chances of complete failure are negligible. Timely repairs can be performed that can lead to the longevity of that equipment. 

How to implement predictive maintenance

Predictive maintenance can significantly impact the productivity and efficiency of critical assets that are prone to frequent damage and require constant repairs. Manufacturers use their existing as well as historical data that is gathered from sensors to create models that anticipate the likelihood of equipment breakdown. With the study and analysis of this data, manufacturers can further appoint measures for the safety of all within the production unit. 

1. Establish the critical assets – The maintenance teams can establish which assets are at greater risk, adding the ones that can induce a higher maintenance cost to be part of the predictive maintenance program. 
2. Build the database –Adequate and appropriate amounts of data are required to initiate predictive analytics for an organization since it is necessary to have both real-time and historical data.
3. Install IoT sensors – The relevant sensors need to be attached to critical assets. For any information that is missing from the database, the sensors can gather that data. Also, real-time data can be fed to the maintenance program, essential for the early detection of abnormalities. 
4. Execute Advanced analytics – All the data collected will be put through software with distinct algorithms for unique issues. 
5. Schedule maintenance – Finally, the software will provide technicians with insights into the health of the equipment and warnings in case of any exigencies to trigger replacement and repair. 

Conclusion

Predictive maintenance proactively addresses problems associated with maintenance and in the process, saves costs and improves the overall efficiency of the entire operation. With the increasing availability of powerful machine learning and AI technologies, predictive maintenance is becoming more accessible than ever before. As technology develops, more sophisticated predictive maintenance tools are set to emerge.