The Value of Predictive Maintenance in Preventing Downtime and Reducing Costs

Maintenance is often viewed as a necessary expense by management, especially when it comes to operational technology and devices. However, recent years have seen a shift in perception, with management recognizing the value of maintenance. Maintenance contributes to business processes in many ways, such as asset preservation, cost reduction through optimized maintenance, and prevention of malfunctions and breakdowns. With advancements in technology and the innovation of the Internet of Things (IoT), maintenance strategies have evolved into a more advanced approach known as predictive maintenance.

Predictive maintenance, or PdM, is a maintenance strategy that focuses on preventing downtime. This strategy involves the use of monitoring equipment, such as sensors and smart technologies, to identify parts of a machine that are likely to malfunction and provide notifications to the maintenance team during the operation process. There are various types of predictive maintenance that can be carried out depending on the factory operations.

The first type of predictive maintenance is vibration analysis. When a machine is running, it produces vibrations. Vibration analysis refers to the process of monitoring vibration data within the running machines, such as the levels and frequencies of vibration. In vibration analysis, the gathered data will be compared to the acceptable operation vibration limits of the machine. Breaching these limits shows that the health condition of the machine is declining. By detecting these issues early, maintenance can be scheduled before a breakdown occurs, minimizing downtime and costs.

The second type of predictive maintenance is acoustical analysis. There are two methods of gathering data through acoustical analysis: sonic acoustic analysis and ultrasonic acoustic analysis. Sonic acoustic analysis focuses on obtaining data from low- and high-rotating machinery. The example of data obtained by sonic acoustic analysis is the lubrication levels of equipment. On the other hand, ultrasonic acoustic analysis gathers data from the sound created by machineries with a range of frequency that cannot be heard by human ears. For example, the data on the level of stress and friction of equipment can be obtained through ultrasonic acoustic analysis.

The third type of predictive maintenance is infrared analysis. Infrared analysis detects high-temperature conditions emitted due to worn-out equipment and malfunctioning electric circuits using infrared cameras. These cameras can detect temperature changes that may indicate a problem before a failure occurs.

Implementing predictive maintenance as a future maintenance strategy can provide numerous benefits to an organization. By detecting issues before they lead to downtime, organizations can minimize costs associated with equipment repairs and production losses. Predictive maintenance can also help to prolong the lifespan of machinery, thereby reducing the need for costly equipment replacement. Additionally, predictive maintenance can contribute to overall safety and compliance by identifying and addressing potential issues before they become major problems.

In conclusion, predictive maintenance is an advanced approach to maintenance that can help organizations prevent downtime and reduce costs associated with equipment repairs and production losses. With the implementation of predictive maintenance, organizations can ensure the preservation of assets, optimize maintenance, and prevent malfunctions and breakdowns. As technology continues to advance, predictive maintenance will continue to evolve, providing even more benefits to organizations that choose to implement it as their future maintenance strategy.