From remote to predictive maintenance: How IoT refines a classic M2M concept
Remote maintenance services have been used in the manufacturing industry for many years to maintain spatially distributed machines and equipment. Using a dedicated line, service engineers can establish a connection to a machine and access its control system. Depending on the available transmission mode and access rights, the engineer can provide passive assistance to on-site machine operators or even take active control of the system. The benefits are obvious: remote maintenance slashes travel and personnel costs while improving customer service by offering faster response times.
This recent surge in demand has fueled a boom in the availability of remote maintenance software for industry applications. However, many of these programs lack the necessary flexibility and “intelligence.” Typically, a separate PC or desktop environment must be set up for each active machine. In addition, the applications are not usually integrated in the existing system environment, so the data they collect cannot be incorporated into these systems unless it is entered manually or copied from a USB stick. However, these problems need not exist as technology already provides everything needed to address these issues.
Taking IT-based maintenance to a new level
Modern M2M software replaces the rigid structure of isolated local computers and 1:1 connections as it offers the possibility of connecting, managing and controlling an unlimited number of machines in virtual networks. This way, service engineers have an overview of all machines and can take action in the operating process remotely from one single access point. This provides a great benefit, but what manufacturers really profit from is the availability of all machine data.
Aggregating and analyzing usage data
As Steve Hilton, guest author and lead analyst at Analysys Mason, describes it in his posts, we are transitioning from an M2M world to the Internet of Things (IoT), where the value lies in aggregating and analyzing usage data. In the context of machine maintenance, we will see the value of analyzing machine data to predict malfunctions. Being able to identify asset failures before they occur allows companies to take corrective action in advance and increase machine uptime. Since predictive maintenance solutions are based on the analysis of usage data, they become more powerful over a period of time as manufacturers gain more insights about anomalies in correlation with usage data.
While predictive maintenance will help machine operators save costs by reducing unplanned downtimes, it will allow machine manufacturers to improve their business as well. For example, knowing about machine failures helps manufacturers plan and offer new maintenance services based on guaranteed uptime or optimize warehousing costs by having spare parts only on stock when they are actually needed. Moreover, it will help to increase product quality and shorten release cycles.
Predictive maintenance is only one example in the manufacturing industry where the intelligent usage of data adds value to existing concepts. In the same way, the IoT will offer new possibilities in optimizing production or logistics processes.