How Predictive Maintenance is shaping Industrial Enterprises
Manufacturers turn to the Industrial Internet of Things for a smarter approach, continually behaviour data to inform actionable insights that predict product failure, increase uptime and enhance asset efficiency. Maintenance is a strategic concern when developing and manufacturing products, but a third of all maintenance activities are carried out too often, and half are ineffective. For machine operators and factory managers, preventative maintenance and asset repairs consume unnecessary resources, eat into operational costs and cripple efficient operations. In such cases, Predictive Maintenance could be helpful.
Maintenance and Repair Operations (MROs) are crucial for the proper functioning of enterprise assets for being a key to the continuity and effectiveness of business operations. MROs involve a wide range of activities such as inspecting industrial installations, repairing machinery, replacing damaged or malfunctioning parts.
Difference between Preventive Maintenance and Predictive Maintenance
In the past, businesses maintained their assets reactively. Enterprises repaired or replaced an asset after it had broken down. The asset was restored to its original condition. Reactive maintenance was problematic because it is linked with equipment breakdowns that stop activities and lead to massive losses.
To reduce the inefficiencies of reactive maintenance, industrial organisations transitioned to the preventive maintenance model. Preventive maintenance schedules repair and service operations at regular intervals to prevent equipment failures. In short, preventive maintenance considers the assets’ expected lifetime to inspect and maintain them proactively. By following this way, it will alleviate unexpected downtime and stimulates the continuity of business operations.
Predictive Maintenance Strategy
Preventive maintenance is currently dominating the enterprise maintenance approach. Nonetheless, it is far from optimal because it generally maintains assets earlier than their end of life. Therefore, it drives a sub-optimal Overall Equipment Efficiency (OEE). Enterprises must plan maintenance activities based on factual information about the assets’ status instead of hypothetical EoL values to achieve optimal OEE.
Predictive maintenance offers many business benefits when compared to the conventional reactive and preventive models. These include improved asset utilisation and OEE, avoidance of unscheduled downtimes, and optimal planning of maintenance activities.
Predictive Maintenance as an Industry 4.0 Application
Predictive maintenance’s benefits translate to significant cost saving and increased revenue for business enterprises that manage large deployments. Despite that, predictive maintenance is still not widely installed, given that getting access to timely and detailed information about the assets’ condition is quite challenging.
In the past years, the scenario is gradually changing because of the proliferating deployment of sensing systems and advanced digital technologies like the internet of things (IoT), big data, artificial intelligence (AI) in large organisations’ production facilities. The deployment of these technologies are called fourth industrial revolution (Industry 4.0).
Industry 4.0 is deploying sensors and cyber-physical systems to digitalise physical processes and implement IT-based data-driven automation and control operations. It enables a rich set of industrial applications like flexible automation, predictive maintenance, digital twins, and various supply chain management optimizations.
To maintain enterprises, Industry 4.0 facilitates the collection of massive volumes of digital data about the condition of machinery and equipment. This data collection is empowered by deploying different sensors such as vibration sensors, acoustic sensors, temperature sensors, power consumption sensors, and thermal cameras.