Omron Corporation

Yoshiyuki Morita

Kazuki Kasai

Hiromasa Takatsuka

Atsushi Irie

Taiji Yoshikawa

Koji Takizawa

Koichi Furusawa

Hidenobu Umeda

Atsushi Osada

Naoki Fujimoto

Yukio Ogawa

Kiyotoshi Okura

A battery selection device has a preparation amount information acquisition component, a supply amount information acquisition component, a battery candidate extraction component, and a determination processor. The preparation amount information acquisition component acquires preparation amount information indicating the scheduled date and time of a loan and the required charge amount. The supply amount information acquisition component acquires supply amount information indicating the full charge power capacity and the current charge amount of each battery. On the basis of the preparation amount information and the supply amount information, the battery candidate extraction component selects loaner battery candidates, which are batteries that can be charged to the required charge amount by the scheduled date and time of the loan. The determination processor picks out a loaner battery on the basis of the predicted progress of deterioration of batteries.

Battery selection device, battery selection method, battery selection program, and recording medium

The object of this invention is to produce a monitor which can correctly distinguish normal from abnormal vibration using automatically selected monitoring features and algorithms. The monitor can be used with a variety of devices with different vibration characteristics. The monitor according to this invention has a sensor that detects the vibration of an object. The monitor is operated in learning mode when the object is vibrating normally. The waveform of the vibration detected by this sensor is sampled over an appropriate period and analyzed. The data for each sampling feature are subjected to statistical processing, and the data which evince slight change are extracted as monitoring features. A monitoring algorithm is automatically made for these monitoring features. The algorithm selected in learning mode are used to evaluate the vibration of an object when the monitor is in operating mode. The operator does not need to set up the algorithm, and can monitor the object with a more reliable algorithm than the conventional one selected by fixed monitoring features.

Vibration monitor and monitoring method

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Kyoto, Japan

Yoshiyuki Morita