Predictive maintenance consists mainly in anticipating maintenance needs in order to avoid a production stoppage resulting from a machine breakdown. It is a concept linked to computer-assisted maintenance management aimed at optimizing the production line. The expansion of Big Data also implies the optimization of production. Data and predictive maintenance are therefore essential to the company.
Data and industry
Data is attracting more and more players in the industry after having been mainly linked to personalized marketing. Data can now answer many production-related questions, such as machine lifecycles and production line optimization. It is also necessary to predict when a component is likely to fail. Companies are then forced to turn to predictive maintenance.
Predictive maintenance arises because of a combination of factors. These factors include the increase in the number of sensors installed on machines, which corresponds to the IoT. Another factor is the greater demand for the use of Cloud computing. This type of solution is very popular for data storage and analysis. This type of solution adapts very well to the needs of each customer.
Predictive maintenance and preventive maintenance
Predictive maintenance should not be confused with preventive maintenance. Preventive maintenance is a much older practice that consists in organizing a schedule of interventions at specific times. It is simpler but not as complete as predictive maintenance.
Predictive maintenance is different in that it is rather aimed at predicting when a machine or equipment may fail. Predictive maintenance therefore avoids slowing down production because maintenance takes place before the failure. Reliability is increased and maintenance costs are reduced. Customer satisfaction is of course improved.
The objectives of predictive maintenance
The main objectives of predictive maintenance are to reduce breakdowns and decrease the frequency of repairs that delay the assembly line. This method makes it possible to anticipate the precise moment of failure. The probability of damage is also identified. The company can therefore proceed with repairs at the most appropriate times so as not to affect the production rate.
An efficient method
It is important to combine methods effectively to optimize the predictive maintenance process. Maintenance must follow certain steps. A program must be put in place by drawing up a list of parts to be monitored. It is also necessary to establish alert thresholds. Another important step is to define verification periods. The industry must also update the procedure in the maintenance plan.
An increasingly common predictive maintenance method is the acoustic method, which uses ultrasound to evaluate machines. With ultrasound, it is possible to detect a mechanical defect, a leak, or an electrical problem, for example.
It is also possible to use thermography. The equipment allows monitoring the thermal profile of machines. Temperature sensors and infrared cameras help to prevent a leak or can indicate a hot spot. It is also possible to prevent a sealing problem. This method does not involve any contact with the machines and therefore does not lead to any interruption in production.
The thermographic method can be complemented by vibration analysis. The vibrations emitted by the machines are captured and analyzed. This method helps to detect a tightening or alignment error as well as the wear of a part. Amplitude, decibels, or frequency are used as units of measurement for this method.
An important investment
Although the promises of predictive maintenance are interesting, you must be prepared to pay a significant amount of money up front. It is only in the long term that the benefits will appear. Because the volume of data is so large, implementation is complex and expensive. Answers are slow in coming and are not provided immediately after implementation.
The contribution of the business team is essential, and here again, patience is required before the new operating mode is fully assimilated. The business team must use its experience in the field.
Internet of Things and Predictive Maintenance
The analysis of the data made possible by the Internet of Things is what leads to the anticipation of possible defects of the machines. The sensors on the machines transmit the data to a software program. They are then analyzed, and it becomes possible to determine the probability of a defect occurring. Failures can also be anticipated. The machine can be repaired at the most appropriate time.
The relevance of predictive maintenance
Industries that want to adopt predictive maintenance must first evaluate the potential benefits of this approach. It is first necessary to audit the production lines. It is also necessary to measure equipment components using the Internet of Things. All the data is studied.
The analysis of the data comes after they are collected. It is important to ensure that the data is in line with the company's objectives. It is at this stage that we must seek to understand the behaviours. The company uses data science to conduct intelligent analysis. This analysis, if done properly, helps to discover anomalies thanks to data visualization and machine learning, among others.
The creation of models
Thereafter, the company must adopt a defect detection model. After data preparation, business experts perform data analysis and experiment with models. The reliability of the adopted models is determined by the relevance of the event history. When the models are validated, they are placed in production in the analysis chain. The data flows implemented allow the method to be optimized.
It is important to respect all the steps of predictive maintenance before arriving at the final result. The company must move to a scalable solution to move to a higher dimension without difficulty. Predictive maintenance is particularly effective in the case of a targeted initiative. When dealing with a group of plants, Predictive Maintenance becomes more complex to apply.
Of course, the company must manage the data efficiently. The data asset must always be assessed first. It is important to target the most relevant information. It is also necessary to understand the nature of the relationships between these data. Within the company, knowledge of business issues is essential for predictive maintenance to fully fulfill its functions.
The implementation of continuous analysis models is one of the key elements of predictive maintenance. The company must target its needs to determine to what extent it should use predictive maintenance. The successful application of predictive methods leads to significant savings in the long term, even if it requires a high initial investment. The results will be visible in the long term.
To learn more about the implementation of this type of solution, Ryax helps you in the analysis of your data.
La Ryax Team.