Received the Japan Society of Mechanical Engineers Award

Ebara Corporation (hereinafter referred to as Ebara) was awarded the Society Award from the Japan Society of Mechanical Engineers (hereinafter referred to as JSME) ^※2 in recognition of its paper "Research on a sliding surface condition monitoring system using machine learning" ^※1.

In its medium-term management plan, "E-Plan 2022," EBARA aims to strengthen its development capabilities to create a competitive advantage in its existing businesses, and is working to strengthen its S&S business overall and create synergies through collaboration between business divisions. We are also building a system that can provide optimal services to each market, customer, country and region.
It is said that most of the breakdowns in rotating machinery equipment are caused by wear of the machine elements. Conventional diagnostic methods mainly involve time-planned maintenance, which involves calculating the life span based on experience, conducting regular inspections at set intervals, and diagnosing abnormalities by relying on the many years of experience and five senses of skilled workers. However, recent research has shown that condition monitoring maintenance methods, in which rotating machinery equipment is constantly monitored and maintenance is carried out at the appropriate time based on signs of failure, are becoming mainstream.

In this research, we went a step further and built a system that uses machine learning to automatically and accurately detect anomalies from data collected from multiple sensors and even identify their causes, thereby conducting fundamental research into optimizing learning methods in machine learning.

• It is believed that data collected during condition monitoring for the purpose of machine maintenance will contribute to the construction of models that can predict failures. In addition, machine learning technology is believed to be applicable to condition monitoring maintenance.
• Current diagnostic technologies are mostly aimed at rolling bearings, which have a high failure rate, and there has been little technical research on sliding parts such as plain bearings, so valuable research data has been obtained.
• This technology can be applied to detecting tribo-anomalies, a common issue in mechanical engineering, and has a wide range of potential applications.

We will continue to conduct research so that this research will contribute to technology that extends the lifespan of rotating machinery equipment and help solve social issues by improving the efficiency and reducing costs of maintenance and inspection work.


By addressing key ESG issues based on our long-term vision and medium-term management plan, the EBARA Group aims to achieve the Sustainable Development Goals (SDGs) and further increase its corporate value.