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Since construction machinery manufacturers recommend various brands and types of greases for their machinery, customers would benefit from a standardized grease which can be used in all construction machinery. Furthermore, construction machinery manufacturers have many experiences of field problems caused by commercially available and commonly used EP Lithium greases. Therefore, the Fuels and Lubricants Committee of Japan Construction Mechanization Association (JCMA) has developed a new grease specification called “Japan Construction Mechanization Association Specification (JCMAS) GK,” for construction equipment. The JCMAS GK includes requirements for National Lubrication and Grease Institute (NLGI) No. 1 and No. 2 consistency grades. The JCMAS GK greases have enough lubricating properties for periodical grease fitting of most construction machines, hydraulic excavators, bulldozers and wheel loaders. The JCMAS GK greases are applicable from -20 to +130 degrees Celsius.

Hydraulic fluid (HF) specifications for mobile construction equipment called JCMAS HK and HKB have been established by the Fuels and Lubricants Committee of Japan Construction Mechanization Association (JCMA). The specifications are designated by two viscosity categories of single grade and multigrade. Each category has ISO viscosity grade (VG) 32 and 46. The JCMAS HK oils are recommended for use in hydraulic systems designed at pressure up to 34.3MPa(5000psi) and to heat hydraulic fluid up to 100 °C. These oils also provide wear control, friction performance, oxidation and rust protection, seal swell control and filterability performance. Two piston pump test procedures were developed to evaluate lubricating performance of these oils under high pressure conditions. The JACMAS HKB oils are classified as environmentally friendly oils due to the additional requirement for biodegradability.

Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users.