Predictive maintenance directly monitors the condition and performance of production assets to predict and identify the optimal time for them to be serviced or for parts to be replaced (maintenance). Predictive maintenance solutions use data from various sources – including the production assets – which minimizes the reliance on human judgement. By using data – and not only human judgement – predictive maintenance systems can identify underlying issues that would otherwise go undetected until it’s too late. In this way, predictive maintenance enables organizations to improve the accuracy of their maintenance plans, optimize servicing costs, maximize the lifespan of production assets and minimize disruption to business operations. As a result, the business is able to operate with greater confidence and can build stronger trust and relationships with both customers and suppliers.
The main differences and advantages of predictive maintenance over standard warning alerts are:
Predictive maintenance systems are based on making use of the right data and the analysis of that data using appropriate algorithms. In practice, the implementation of a predictive maintenance system starts by gathering the data from available sensors and evaluating which data can be used to provide insight into the state and performance of an asset and its vulnerability to failure.
Once the data parameters have been agreed, a system is developed and implemented, and a dashboard is created that provides information about the possibility and likelihood of failure at any given time. Over time, models can be enhanced to include data such as production capacity, total days in use (age) and date of last service. Depending on the nature of the machinery and the organization’s requirements, a predictive maintenance system can be further enriched to include information such as seasonal and ambient temperatures (e.g. do hot temperatures increase strain and/or demand) and historical sales data of the company (to anticipate spikes and lulls in demand) so that manufacturers can optimize their production operations (including staffing), maximize the value they derive from their machinery and reduce the likelihood of unanticipated downtime.
Over time, predictive maintenance systems can be integrated with all sorts of enterprise systems, such as customer orders, staffing, marketing and sales activities (e.g. promotions) and seasonal events (e.g. Christmas, popular vacation seasons, trade fairs) to improve alignment across the business and ensure maintenance tasks are scheduled at a time that is best for the business.
The chief beneficiaries of predictive maintenance systems are manufacturers, especially those that feel that their maintenance costs are higher than they should be, have experienced excess downtime or too often act in a responsive way and with disruption to their supply chain operations.
Predictive maintenance will be easiest to implement and will deliver the greatest benefit in those instances where production volumes are high, and where products being manufactured are common in nature. The more variables that enter the equation (e.g. different types of products produced by the same machinery, frequent configuration of machinery, high variance of difficult-to-compare tasks being performed), the more difficult it becomes to achieve a high degree of accuracy.
The number and grouping of sensors on machinery will also contribute to greater accuracy and effectiveness of a predictive maintenance system. Sensors and devices should be configured in such a way that data flows to a central point and is available for deeper analysis (ideally, in real time). Much of the industrial machinery built today features IoT sensors as standard, but having older machinery doesn’t preclude a business from benefiting from predictive maintenance. The number of ‘bolt-on’ IoT sensors available on the market continues to grow, ranging from relatively simple (e.g. pressure and temperature), though to more advanced sensors, like thermal-imagery cameras, which can be used to detect leaks or areas of overheating.
After an initial analysis and once it has been established that failures can indeed be predicted, it’s time to implement the solution. When implemented using ‘on premise’ IT, it’s worth considering the creation of your own data lake, which will serve the data in stream mode (not only batch). This, in turn, implies the use of spark streaming.
Alternatively, cloud service providers like AWS and Microsoft Azure offer dedicated architectures that are commonly used in the world of IoT (e.g. AWS IoT Core and Azure IoT) and Industry 4.0, which enables the costs of and time required to implement predictive maintenance systems to be dramatically reduced and also makes their maintenance in the future easier. Given the scope and scale of such solutions, most companies will look to external providers (like ITMAGINATION) to build and implement their predictive maintenance systems.
Probably not. Importantly, though, predictive maintenance will help anticipate and manage the risk of breakdown and should give your business the chance to prepare accordingly (bring forward services or part replacements, switch production to different machines or locations, reduce orders, alert customers, etc.), which means less disruption for your business, greater trust from stakeholders and improved operational efficiency.
Want to discover how you can use your data to reduce production downtime and keep your business on track? Talk to ITMAGINATION about it.
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