Machine failures have caused huge costs for the manufacturing industry. Fortunately, new technologies such as sensors and machine learning can predict in advance and reduce downtime. According to BureauofAnalysis data, the average life span of industrial equipment in the United States has continued to increase since the 1970s, and the average usage time has now reached 10 years. As assets age, the opportunities for failures and repairs become more and more.
Industry 4.0 offers new technology that can quantify downtime and monitor and predict failures
EitanVesely, co-founder and executive officer of Presenso, a predictive maintenance company of the Internet of Things, said that many manufacturers still adopt the “Run-to-failure†approach, which is passive maintenance, but it’s actually an expensive cost. In addition to repairing the malfunctioning machine, this will also damage the entire production line.
As the number of years of manufacturing machines continues to increase, the costs and risks of returning from manufacturers also rise. Many companies have not accurately quantified their downtime costs, but it is estimated that even small plants can generate up to tens of thousands of dollars per hour.
Industry 4.0 offers new technologies that can quantify downtime and monitor and predict failures. The sensors in the machine can quickly show abnormal trends and problems through advanced algorithms.
General manual methods need to take a list, and set a lot of variables, and make evaluations based on historical data; the machine has a complicated method of execution, can consider tens of thousands of variables, and detect subtle changes in real time, which is the traditional method Can not do it.
The Infosys Global Survey noted that while 81% of manufacturers understand that machine learning helps machine maintenance, only 17% actually use the technology. Many small and medium sized businesses are still using the 1980s system, even using manual data analysis.
Manufacturers that have not yet introduced predictive analytics may ask, how high is the ROI for doing so? Using predictive analytics requires investments in sensors and SaaS solutions. Manufacturers should understand the accuracy of relevant analysis tools, consider the impact of downtime, and then make a comprehensive assessment.
Vesley believes that from the beginning of proof-of-concept, if it is possible to detect asset failures in advance, it is easier for the management to accept such proposals. Cross-sector cooperation, including the integration of the IT department and the operating department, is also very important.
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