In recent years, the finite resources of the earth and excessive environmental burdens have lead to great need for new ways of maintaining machines that focus on resource circulation and reduction in environmental impact so that contemporary expendable society is converted to sustainable society.
Conventional machine maintenance employs corrective maintenance in which a machine is repaired after it breaks down, or uniform preventive maintenance which is performed at predetermined intervals. Corrective maintenance entails a great deal of time and cost for repair. Preventive maintenance generates unnecessary part and oil waste due to its uniformity and thereby imposes greater costs on customers. Further preventive maintenance is expensive because of the intensive labor required. There is a requirement for a departure from such conventional maintenance manners and for conversion to predictive maintenance in the future.
In predictive maintenance, the degree of soundness is diagnosed by understanding data of load and environment during operation, a database of past maintenance history, physical failure and others and further deterioration and remaining life are predicted in order to anticipate a defect on a machine at an early stage and to provide a safe operation environment.
Normally, in such a system employing predictive maintenance, sensors installed in an object machine detect an operation state of the machine, a data collector installed in the machine collects the raw data representing the operation state and sends the raw data to a computer in a management center (for example, a service department of a company in charge of maintenance of the machine) in real time or at predetermined intervals. Upon receipt of the raw data, the computer analyzes the raw data and diagnoses the soundness of the machine.
However, the amount of raw data collected by the data collector is huge and is sent from the machine to the management center through telecommunications, which may be unreliable and costly. One solution is compression of the raw data and sending of the compressed data to the management center. For example, the Patent Reference 1 discloses a method for compressing time-series operation signals obtained by sensors into histogram data or frequency distribution data. Further, the Patent Reference 2 discloses a method for modifying intervals to send an operation signal in accordance with a failure probability (a bathtub curve), and the Patent Reference 3 discloses a method for accumulating a frequency of detection per unit time in order to save memory capacity and judge the state of an object machine from the histogram.
Patent Reference 1: Japanese Patent Application Laid-Open Publication No. 2003-083848
Patent Reference 2: Japanese Patent Application Laid-Open Publication No. 2002-180502
Patent Reference 3: Japanese Patent Application Laid-Open Publication No. HEI 10-273920