Hitachi Industrial Equipment Systems’ ML Predictive Diagnosis Service For Air Compressors
Hitachi Industrial Equipment Systems Co., Ltd. announced its “Predictive Diagnosis Service” for air compressors used as power sources for factory equipment.
The service uses machine learning (ML) to analyse data obtained through remote monitoring and combines it with knowledge accumulated by Hitachi Industrial Equipment Systems maintenance staff to detect and prevent problems and abnormalities that could lead to equipment stoppages in advance. This service also uses maintenance staff’s expertise to estimate the effects of factors that reduce operating efficiency and can propose more efficient operations with less environmental impact.
To ensure the stable operation of air compressors, specialised technicians perform maintenance and inspections. However, due to declining birthrate and aging population, the number of technicians is decreasing, and there is an urgent need for remote maintenance management and more efficient operations. From the perspective of preventing global warming, there is a growing need to reduce environmental impact by operating equipment more efficiently with lower power consumption.
Since October 2017, Hitachi Industrial Equipment Systems offered FitLive, an equipment monitoring service, which reduces the equipment downtime by monitoring the operating status of each product remotely and automatically sending alerts when problems occur. Analysis of FitLive data for air compressors revealed that temperature-related alarms and malfunctions account for approximately 75% of all alarms and malfunctions.
A predictive diagnostic service which uses ML to estimate the future effects of temperature rise trends detected by sensors and take preventive measures against malfunctions was developed. FitLive systematises the decision-making know-how accumulated by Hitachi’s maintenance staff during their maintenance work and uses it as a basis for decision making.
Predictive diagnostics are performed by a combination of ML and know-how. The results and the factors on which the estimation is based are displayed. Maintenance staff previously inferred and provided consulting based on the data they had obtained, but this service enables maintenance staff to make more specific and effective proposals by automatically performing predictive diagnosis.
The data can also be used to identify cases in which the performance of an air compressor tends to deteriorate, such as when the ambient temperature of the air compressor is high or the filter needs to be cleaned, and to propose more efficient operation methods.
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