Predictive maintenance has proven to be an effective methodology for enhancing the lifespan of valuable assets in the industrial sector, as it allows companies to predict when equipment is likely to fail or stop. This way proactive measures can be taken to prevent downtime and reduce maintenance costs.
It’s true that with the rise of new technology, predictive maintenance has been greatly enhanced through the combination of advanced analytics and machine learning algorithms. However, understanding the right moment of intervention has still been a gap in this maintenance strategy.
Defining Predictive Maintenance
It consists of a repairing strategy that utilizes digital data to predict equipment’s status and this way provides the necessary insights for the understanding of when equipment is going to fail or stop, and through this collected information schedule intervention activities at an optimal time.
The basic idea behind predictive maintenance is to replace reactive, time-based maintenance with proactive maintenance that is performed only when needed.
It usually relies on data from sensors, historical maintenance records, and equipment usage patterns to identify potential problems before they occur.
This approach involves continuously monitoring equipment to gather data and using predictive analytics models and machine learning to enable the identification and processing of equipment and machinery data.
An adequate predictive maintenance strategy has as its goals reduced downtime, prolonged equipment life, and increased efficiency by performing maintenance only when necessary, rather than on a fixed schedule.
Predictive maintenance is becoming increasingly important as companies seek to optimize their operations and reduce maintenance costs.
Exploring predictive maintenance bottlenecks
As mentioned before, the employment of digital technologies in the support of predictive maintenance has been enabling the real-time monitoring of necessary data to feed predictive models with knowledge for predicting issues.
In addition, if systems are not integrated, it can be challenging to get a complete picture of the equipment’s health, which can result in incorrect predictions. As a result, companies may end up performing a lot of predictive maintenance without fully understanding the optimal time for intervention, leading to unreliable and inefficient practices.
This can result in significant resources being spent without achieving the desired outcome, leading to decreased efficiency, increased costs, and lower equipment reliability. To ensure the success of predictive maintenance, it is important to have access to accurate, contextualized, and trustworthy data, as well as to have integrated systems in place.
How integrating systems can enable a new perspective for predictive maintenance
Businesses must look forward to implementing digital systems that can connect each part of the operation and maintenance process. This will allow them to streamline their processes and gain a comprehensive view of their equipment’s health.
By having a single, integrated system, companies can access accurate and trustworthy data quickly and easily, which will help them make informed decisions about when and how to perform maintenance without any unnecessary overlapping.
The use of digital systems can help businesses avoid the pitfalls of unreliable and inefficient maintenance practices
Implementing a digital system that is able to connect field information to ERP and CMMS is essential for companies seeking to improve their predictive maintenance practices and achieve their desired outcomes.
Predictive maintenance is a crucial aspect of modern maintenance practices, allowing organizations to proactively identify and address potential issues before they escalate into bigger problems. However, for predictive maintenance to be effective, it is essential to have reliable and connected data systems that can provide relevant information to the predictive models.
Without trusty data and a connected infrastructure, predictive maintenance can easily fall short, resulting in ineffective decision-making and missed opportunities to improve maintenance processes. Thus, organizations must ensure that they not only implement predictive maintenance practices but also invest in data and technology infrastructure to support them. Only then can they fully realize the benefits of predictive maintenance and drive improved maintenance outcomes.
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