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Autonomous maintenance is one of the key features of TPM (AM). This method of maintenance makes everybody responsible for the efficiency and maintenance of the system.
Fremont, CA: With the emergence of industrial artificial intelligence (AI) and the Internet of Things (IoT), companies in all sectors are being reimagined with apps. Companies are learning how to use their data not only to evaluate the past but also to forecast the future.
Maintenance is a key field that can drive substantial cost savings and output value around the world. The cost of computer downtime is high: according to the International Automation Society, $647 billion is lost worldwide every year. Over the years, organizations have updated maintenance procedures to reduce downtime and increase performance. There still seems to be uncertainty, however, on the best way to use data in the search for optimal operating performance.
AI's position in total production maintenance
Total Productive Maintenance (TPM) is a comprehensive framework for maintaining and improving essential assets and operating processes that results in fewer breakdowns, fewer downtimes, increased production and improved safety. Established in the 1960s, many industrial businesses are now using this approach to proactively complete machine maintenance on the basis of historical data and schedules when repairs are likely to be required. The goal of TPM is to increase overall Equipment Effectiveness (OEE) and plant efficiency using scheduled maintenance principles. With routine maintenance of equipment, you can prevent breakdowns and increase the uptime of properties.
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AI and Autonomous Maintenance the adoption and implementation of
Autonomous maintenance is one of the key features of TPM (AM). This method of maintenance makes everybody responsible for the efficiency and maintenance of the system. Maintenance of equipment shall be carried out by the machine operators themselves, instead of maintenance technicians being the only ones to fix the assets. This method of maintenance makes everyone responsible for system efficiency and maintenance. Maintenance of equipment is carried out by the machine operators themselves, rather than maintenance technicians being the only ones to fix the assets. AM is also difficult to execute because it requires a lot of communication and training. Machine operators lack the historical machine experience that technicians have, and technicians might not be so eager to give up those tasks without foresight on new tasks on the horizon.
Businesses can now take advantage of AI-driven technologies that make the adoption of AM easier. The frontline operators can understand their computers even better than before. Having all the historical data in one easily accessible dashboard keeps everyone in your business on the same page and makes it easier for machines to get serviced faster. Now, companies can ensure that every operator has the right resources and skills at the right time to get the job done.