Running high-volume manufacturing smoothly will involve the perfect functioning of all the machines, resulting in efficient production. The objective of any manufacturing unit is to keep operations at optimum speed with very less downtime. However, every equipment goes through wear and tear and needs servicing and maintenance periodically. The critical question here is, when is the best time to conduct equipment maintenance. Scheduled maintenance is effective or damage control in case of machine failure?
Different Methods of Maintenance
Preventive: This approach involves performing maintenance activities as per a fixed schedule irrespective of the current condition of the pieces of equipment. This is similar to a regular health checkup or periodic car maintenance. This approach is simple to implement; however, it might result in delayed action in case of damage. It can also involve unnecessary efforts when the maintenance is not needed.
Conditional: Conditional approach enables maintenance activities based on the existing working conditions of the pieces of equipment. The conditions are usually monitored through manual inspection or from embedded sensor data. This ensures prompt action without causing delays in case of probable failure. The primary drawback of this approach is the dependency of maintenance activity on the reported data. The maintenance can begin only after a data-based failure may be reported. This might cause delays when the maintenance might interfere with production scheduling.
Predictive: This is the smartest approach where the goal is to predict the required maintenance actions to the earliest. This approach involves conditional monitoring as well as a predictive model to determine probable failures. This method is by far, the most effective in optimizing maintenance as per the equipment's working conditions and a manufacturing unit’s production capacity. The challenge here is the complexity involved in implementing predictive maintenance systems.
The main advantage of predictive maintenance is it enables effective planning of maintenance activities in manufacturing units with production scheduling in tandem. With timely action, chances of equipment failures can be drastically reduced by applying the right resources and methods for maintenance. This approach prevents unplanned failures and downtime to a large extent. Furthermore, predictive maintenance can also prevent accidents and hazardous effects of failed or inefficient machinery. Increased longevity of machines can also be an outcome of timely maintenance.
Predictive Maintenance in Digital Revolution
The current scope of predictive maintenance is the result of a surge in digital transformations amongst various industries and businesses. The increasing digitization has opened gates for data-based analytics which can boost organizational decision support systems. In the current digital ecosystem, almost every activity generates data that can fuel a predictive maintenance system. This huge amount of data, which is generally referred to as big data has unlocked many opportunities for predictive maintenance using artificial intelligence techniques.
How to build a Predictive Maintenance system
Even though building a Predictive Maintenance system is a fairly complex process, we can outline a few steps to get started.
-Installation of automated monitoring devices on the targeted machines. This may involve the application of visual devices, measuring vibrations or temperatures, measuring sounds, humidity, etc.
-Embedded processing od data to convert the captured raw data to turn into useful information that can be processed.
-Exchange of information from the supervising system to the local system. This communication should be private and secure to ensure efficiency.
-Creating a predictive model based on data collected and past failure data. Usage of machine learning models can be effectively applied here.
Building a robust predictive maintenance implementation requires a highly efficient digital culture which involves adapting to new technologies for enabling data-driven decision-making.