Predictive maintenance technologies are designed to assist in determining the condition of in-service equipment to estimate when maintenance should be carried out. This strategy offers price savings over routine or time-based preventive maintenance, as duties will only be done if warranted. It is therefore considered as condition-based maintenance performed as suggested by calculations of an item’s degradation state.
Why does predictive maintenance matter?
The most advanced form of maintenance presently accessible is predictive maintenance (PM). Organizations bear the threat of performing excessive or insufficient maintenance with time-based maintenance. And when required, maintenance is performed with reactive maintenance, but at the cost of unplanned downtime. These problems are solved by predictive maintenance. Companies need a maintenance program to avoid breakdowns that contribute to unplanned downtime of machinery that may, in turn, cause businesses to fail to satisfy client requirements. Unreliable machinery also pushes them through overproduction to maintain excessive inventory.
The advent of Artificial Intelligence (AI) in predictive maintenance
There is sufficient capacity to process large amounts of sensor information quicker than ever with Artificial intelligence (AI) and machine learning. This provides businesses an unprecedented opportunity to enhance and even introduce something new (predictive maintenance) to existing maintenance activities. Manufacturing is one sector that can expect unprecedented profits from AI. While most companies already use some type of preventive or predictive maintenance, AI can revitalize a new productivity period.
Total Productive Maintenance (TPM) is a holistic system for maintaining and enhancing critical resources and operating procedures resulting in fewer breakdowns, fewer downtime, enhanced output, and enhanced safety. Developed in the 1960s, this method has been used by many manufacturing companies to proactively finish machine maintenance based on historical information and schedules of when repairs are expected. TPM seeks to enhance overall equipment effectiveness (OEE) and plant efficiency using planned maintenance concepts. With periodic maintenance of machinery, breakdowns can be avoided and asset uptime can be increased.
With the increase of industrial artificial intelligence (AI) and the Internet of Things (IoT), the software is recreating businesses across all sectors. Companies learn how to use their information not only to evaluate the past but also to forecast the future. Maintenance is the main aspect that can drive substantial price savings and importance for manufacturing worldwide. Companies have overhauled maintenance procedures over the years to reduce downtime and enhance efficiency. However, the finest way to use information in the search for optimum operational effectiveness still seems to be confusing.
Autonomous Maintenance (AM) is one of TPM’s key characteristics. This sort of maintenance leaves everybody accountable for the results and servicing of the device. Machine operators themselves perform machinery maintenance instead of being the only maintenance engineers to repair assets. Through periodic maintenance of resources by machine operators, engineers are freed to focus on bigger modifications to enhance overall machine reliability. As it requires a lot of communication and preparation, AM is often difficult to achieve. Machine engineers lack the historical machine understanding that technicians have, and technicians might not be so fast on the horizon to abandon certain tasks without foresight into new work responsibilities.
Artificial Intelligence and other trends swiftly entering the market of predictive maintenance
Data Mining is a method where big amounts of information are collected and used. The goal is to discover patterns of repetition or connection. The information is organized in such a manner that its use is understandable. In other words, to find helpful patterns in order to use that data later in future circumstances.
Artificial intelligence is the result of combining algorithms that were earlier described with the objective of programming the same capabilities a human has. Simulation of different methods is used, which involves a particular data acquisition to develop an independent learning process and some guidelines based on standardized reasoning.
A sort of independent data-based learning can be found within the artificial intelligence discipline that does not involve program laws or algorithms to determine a conclusion. Machine Learning identifies trends within millions of information units by means of an algorithm and can predict behaviors and make decisions without almost no human interference.
Deep learning conducts a process of learning. It is composed up of a multi-level artificial neural network. The information learned in each level is carried over. This goes on until all the data is merged. The first levels acknowledge particular information that leads in full learning, added to each stage.
The future of predictive maintenance
The predictive maintenance market is still in its nascence. However, both existing and next-generation alternatives will have to be assessed by planners. This extra complexity level is not simple, but it is necessary to consider the fast speed of development. The improvement in maintenance machine learning skills has modified the perception of maintenance. Organizations are enthusiastic about the possibilities accessible to their maintenance teams for possibly the first time. The impact that this concept and its associated applications may have on their ability to optimize their maintenance processes, implement and maintain a predictive maintenance process, and most importantly, save money, is realized quickly by management. Market research experts have estimated that the global predictive maintenance market shows impressive potential to rise at a growth rate of XX% CAGR over the years to come.