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Microchip Technology > Case Studies > Predictive Maintenance for Industry 4.0

Predictive Maintenance for Industry 4.0

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Services

System Integration

The Challenge

Industrial robotic systems see increasing levels of vibration on their mechanical elements which often indicates the need for service. Factory operations personnel need to find a way to increase their awareness of vibration and use AI/ML analytics to interpret the data and address the issue in a timely manner. This is a fairly standard interpretation of predictive maintenance that many Industry 4.0 advocates aspire to.

In the past, service schedules were accommodated using a paper log file, and the intervals between were often decided arbitrarily by a supervisor based on their experience and feel for the operation. As robotic systems in modern manufacturing become more complex, factory managers need to be aware of their maintenance requirements in real-time and be on top of both routine and critical service scheduling in order to avoid an interruption in service.

The Solution

This hyper-aware status and the related collection of data is paramount to a smart factory’s evolution and is best analyzed at the edge.

With Microchip's Total Systems Solution (TSS) approach, Microchip offers a fully integrated, state-of-the-art technology solution for Linux applications, enabling the customers to efficiently design next-gen solutions in a smart, connected and secure manner. This application of IoT technology is ideal for those managing a sensor network while controlling a robot and reporting data through a Human Machine Interface (HMI).

Operational Impact

[Process Optimization - Predictive Maintenance]
Predictive maintenance reduces downtime and repair costs while also extending equipment life and ensuring output quality.

Related Case Studies.

Remote Monitoring & Predictive Maintenance App for a Solar Energy System

The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.

Predictive Maintenance for Industrial Chillers

For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.

Aircraft Predictive Maintenance and Workflow Optimization

First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.

Integral Plant Maintenance

Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).

Asset Management and Predictive Maintenance

The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.

Cutting-edge Predictive Analytics for HIROTEC Group

Hirotec needed to ensure continuous operations and to minimize unplanned downtime in its manufacturing facilities. Unplanned downtime is costly and compromises Hirotec's ability to deliver its goods to customers on time.

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