According to Aberdeen Research, 82 percent of companies have experienced unplanned machine downtime over the past three years, costing as much as $260,000 an hour. Unplanned downtime can result from a wide range of causes, including excessive tool or job changeover, but equipment failure and unanticipated maintenance remain the biggest concerns for most companies, hindering the drive toward the highest possible Overall Equipment Effectiveness score.
For decades, manufacturers have sworn by the principles of Total Productive Maintenance, the Japanese corporate philosophy based on empowering and inspiring all employees to take proactive roles in ensuring the smooth functioning of the facility. A key pillar of this approach is Autonomous Maintenance, whereby shop floor workers carry out basic maintenance of equipment, including cleaning machines, visual inspection and early detection of machine degradation.
It has been estimated that 27% of companies use visual inspection as their primary predictive maintenance methodology. This means that on-site workers are entrusted with carrying out physical inspections, drawing conclusions based only on their own expertise. But the facts and figures speak themselves. Machine downtime remains a growing problem.
The Downsides of Traditional Visual Inspection
Human error, often caused by inadequate training or fatigue, can result in inaccurate and inconsistent findings. There is also an element of subjectivity, since multiple inspectors may be called upon to inspect the same machinery. Then there’s the perennial problem of record-keeping. Once again, human error can frequently affect the quality of inspection records, when data must be manually entered into spreadsheets.
Accidents can happen, especially when industrial equipment is elevated or hard to access. And since safety is a top priority in all industrial sectors, the quality and frequency of visual inspections can suffer, especially when staff are needed for other urgent duties.
Predictive maintenance solutions are, of course, widely used in industry, allowing companies to take some of the guesswork out of the inspection process. Measuring variables ranging from temperature and pressure to vibration and rotation speeds, these systems can be deployed alongside the latest AI-powered approaches such as digital twins.
However, the visual approach still has a vital role to play, in terms of both preventative and corrective procedures. With the right combination of technologies, industrial enterprises can now move toward a unified visual maintenance strategy.
Enhancing Visual Inspections
Two pairs of eyes are better than one. When a machine operator tasked with carrying out a visual inspection is backed up by a remote supervisor, there’s far less room for error. When the operator uses a head-mounted camera or a smartphone to capture images of the machine, an expert can confirm correct functioning. Video and stills can also be recorded for later viewing, and to provide a complete visual record of each inspection.
Automating Visual Inspections
With the rapid rise of Computer Vision AI, enterprises now have the ability to position cameras at critical points along a production line, including difficult-to-access areas in order to automate the entire visual inspection process. Computer Vision algorithms can be trained to recognize and flag up even the slightest deviations in machine components.
Emergency Visual Assistance
When machine downtime occurs, it’s always a race against time to get the line up and running as quickly as possible. But in the case of highly specialized machinery, it can take days for a properly trained technician to arrive on site. That’s when visual assistance comes into play. By establishing a live video stream between the remote expert and the on-site worker, precise visual guidance can be provided through Augmented Reality pointers and annotations, displayed on either a head-mounted system or a smart phone or tablet.
Automated Visual Assistance
The near-term goal of Remote Visual Assistance providers is to achieve the delivery of fully automated guidance for machine repair. The reality is that remote experts aren’t always available to offer real-time guidance to on-site workers, especially when downtime occurs during a night shift or when international time differences are factored into the equation. When every minute counts, Computer Vision-powered self-service hold the key to the fastest and most effective solution. The system is initially trained in a collaborative process between the machine supplier and the Visual Assistance provider. Then, machine learning algorithms enable the system to become smarter over time, recognizing fluctuations in performance and drawing conclusions each and every time an issue occurs.
Toward a Unified Visual Maintenance Strategy
In the Industry 4.0 era, manufacturers need to develop strategies that combine the very best technologies in order to keep machine downtime to an absolute minimum. While next-generation sensors and predictive algorithms are powerful tools, the visual dimension remains key and by marrying Computer Vision AI with human experience and expertise, enterprises can both predict and resolve production interruptions more effectively than ever before. Learn more about how you can reduce machine downtime here.
This article was first published on the TechSee blog.