One of the key elements in Industry 4.0 is Predictive Maintenance (PdM). Beside the rational and benefits we explored in all the previous, today we look at prediction at scale and see how smart factories get more value out of the predictive maintenance and explore deep into the topic what it is all about establishing the right analytic-based maintenance strategy.
As we all know machinery and plants can not tell you whether they are feeling good or bad. But with the widely available smart sensors and devices, we can strategically deploy them to visualise the status across the entire factory, with the modern technologies, and continue to provide availability, and provide early warning status in advance before it really causes the downtime. That is all possible with the Industry 4.0 era.
This is particular important for capital intensive industries, where high uptime is critical to make return on investment, such as oil and gas plants, mining, or typically every countries will classify as mission critical sectors to ensure uninterrupted operations, and if unplanned downtime can completely avoidable and minimize, the better will be.
Predict failures long before they occurs, we transfer the working knowledge of expert in the field into smart sensors, leverage artificial intelligence (AI), digesting the volume of data collected and co-related to the likelihood of upcoming downtime due to early warning from respective component, load conditions, or lifespan for the components etc. By shift toward predictive maintenance (PdM), enterprise reducing unnecessary preventive and corrective maintenance and associated downtime costs, this is why more and more companies are deciding to deploy at scale across their operations. If you have read the news, Sony committed to transform 80% of their existing and coming plants to become smart factory bases to help them to remain competitive in the market they compete in.
Modern technology enables PdM 4.0, i.e. asset-wide analysts system. PdM now in 4.0 too, from PdM 1.0 condition-based maintenance, using sensors trigger alarms based on predefined thresholds, with field operators monitoring a myriad of information. This is why it is called condition-based, as it uses sensors with predefined thresholds as control points. If you reach the sensor, what to do.
PdM 2.0 is equation-based predictions, where equations derived from specific-failure analysis, which allow early identification of potential issues (such as redundancy power supply one is notify for down already, remain one in operate, or unusual high load on the production capability and some of the subsystem produce early warning that if ignore will trigger system downtime). As we move toward more and more computing assisted with data, we are reaching PdM 3.0 where fit-for-purpose analytics suite era. Advanced-analytics models are deployed and used to monitor specific equipment, monitoring for all the critical and non-critical systems and develop specific models to measure the performance, availability and predictive downtime in the model as forecast downtime ahead of time, unless something is acting on etc. PdM 4.0 asset-wide analytics system, is continue expand from PdM 3.0 by deploy full-assett multi source analytics system managed through a monitoring centre to get the end to end visual modelling and know exactly each of the system and subsystem conditions, and the operators is trained on how to respond to various predicted failure events accordingly to standard operating procedures (SOP) with the aim to achieve zero downtime.
Without doubt further, digital transformation toward industry 4.0 transforms the maintenance function and boosts productivity, providing flexibility and predictability that can not be done before that. Because of that, your improved existing computerised maintenance-management systems (CMMSs) become mission critical for zero downtime operations, and a flexible end to end and allow custom visual mapping and show out all the important system and subsystem, provide early predictive downtime ahead and early warning is very important. Feel free to contact E-SPIN for our end to end smart infrastructure performance, availability and continuous monitoring to provide all the early intelligence you need to act on to ensure zero downtime, as well as measurement for all entire system health over time and reporting period. E-SPIN being active in the provide various end to end infrastructure monitoring system, be it deployed for oil and gas for SCADA systems or datacenter for global corporations, to national wide infrastructure or mission critical enterprise application for complete visibility for the traffic, conditions for all the subsystem that make up of the enterprise application to visual mapping for the entire factory facility and plan for the smart factory management.