TECHNICAL FIELD

The present invention relates to a system for diagnosing a sign predictive of failure in equipment by using data acquired by a sensor before and after maintenance.

BACKGROUND ART

PTL 1 discloses a technique relating to this technical field. Claim 1 of PTL 1 recites a configuration in which “A predictive abnormality diagnosis device comprising time-series data acquisition means for acquiring, from machinery that includes a plurality of devices having sensors installed therein and is kept in operation even during a maintenance period, time-series data containing detection values of the sensors, learning means for generating a feature vector representing a state of the machinery and learning a normal model representing a normal range of the feature vector based on the time-series data acquired by the time-series data acquisition means, and diagnosis means for performing a diagnosis of presence or absence of a sign predictive of abnormality in the machinery based on the normal model, wherein, when a learning period of the normal model includes the maintenance period, the learning means learns the normal model based on time-series data on a device that is kept in operation even during the maintenance period among the plurality of devices, and the diagnosis means continues the diagnosis even during the maintenance period”, and in paragraph 0009, an effect is described that “According to the present invention, the predictive abnormality diagnosis device configured to continue a diagnosis even during maintenance of machinery can be provided”. That is, PTL 1 discloses the predictive abnormality diagnosis device capable of performing a diagnosis of a sign predictive of abnormality even during maintenance of machinery.

CITATION LIST

Patent Literature

PTL 1: JP 5480440 B1

SUMMARY OF INVENTION

Technical Problem

As described in claim 2 of PTL 1 that “The predictive abnormality diagnosis device according to claim 1, wherein when the learning period of the normal model includes the maintenance period, the learning means learns the normal model based on a fixed value corresponding to time-series data in at least the maintenance period on a device under maintenance among the plurality of devices and time-series data on a device that is kept in operation even during the maintenance period among the plurality of devices”, when the learning period of predictive diagnosis includes the maintenance period, the detection value of a corresponding sensor is discarded due to maintenance work, and the fixed value is used instead.

In paragraph 0070 of PTL 1, a description is given that “Further, the data mining learning unit161uses, as learning target data corresponding to the device A (maintenance target), the fixed value α corresponding to a detection value in the normal state immediately before maintenance. This makes it difficult for the magnitudes of the detection values of the sensor A1before and after maintenance to deviate from the fixed value α. That is, the normal model for the machinery2is less likely to fluctuate through before, during, and after maintenance, and thus the normal model can be continuously learned in a stable manner”, indicating that the data mining learning unit uses, as learning target data corresponding to the maintenance period, the fixed value corresponding to the detection value in the normal state immediately before maintenance to stabilize the normal model through before, during, and after maintenance.

As described above, PTL 1 discloses the predictive abnormality diagnosis device capable of performing a predictive abnormality diagnosis all the time including the maintenance period, but has no description of construction of a diagnosis model using measurement data before and after maintenance. Any configuration that contributes to the solution of the following problem has not been disclosed accordingly.

(1) The measurement data before and after maintenance contains a number of pieces of data useful for predictive abnormality diagnosis. For example, with greasing up a bearing taken as an example, a low lubrication state brought about by grease deterioration is measured before greasing up. On the other hand, after greasing up, measurement data in a state where lubrication performance has been refreshed by greasing up can be obtained. A sign predictive of abnormality that is a deterioration of lubrication performance can be diagnosed by using a feature that results from converting the measurement data before and after greasing up, the feature representing a difference between before and after greasing up. A mechanism for searching for such a feature is required.

(2) There may be a plurality of features representing the difference between before and after maintenance, depending on a device configuration or installation environment. There are also a number of feature detection algorithms, and considerations must be given to detection sensitivity, processing load, and sensor cost for each algorithm accordingly. Furthermore, the feature serves as input data for performing diagnosis processing. There are also a number of predictive diagnosis algorithms, and optimal diagnosis processing can be performed by using a combination of a predictive diagnosis algorithm and a feature. A mechanism for searching for such an optimal feature detection algorithm and predictive diagnosis algorithm is required.

(3) When a device for use in predictive abnormality diagnosis is a dedicated device that represents a specific physical quantity or a dedicated device specialized for a specific device, there is little expectation for mass production effects, and the device becomes very expensive accordingly. Therefore, a mechanism that allows the optimal feature detection algorithm and predictive diagnosis algorithm searched for in the above (1) and (2) to be reconfigured on a general-purpose device is required.

An object of the present invention is to provide a failure diagnosis system capable of solving at least one of the above (1) to (3).

Solution to Problem

In order to solve the aforementioned problems, a failure diagnosis system according to the present invention is configured to diagnose a sign predictive of failure of diagnosis target equipment and includes a sensor configured to acquire measurement data from the diagnosis target equipment, a pre-maintenance data DB where the measurement data before maintenance acquired by the sensor is stored, a post-maintenance data DB where the measurement data after maintenance acquired by the sensor is stored, a feature detection algorithm group DB where a plurality of feature detection algorithms are stored, a first feature detection unit configured to detect a feature based on the measurement data by using each of the plurality of feature detection algorithms read from the feature detection algorithm group DB, an algorithm search unit configured to select one of the plurality of feature detection algorithms based on the feature detected by using each of the plurality of feature detection algorithms, a second feature detection unit configured to detect a feature from the measurement data by using the feature detection algorithm selected by the algorithm search unit, and a predictive diagnosis processing unit configured to diagnose a sign predictive of failure of the diagnosis target equipment by using the feature output from the second feature detection unit.

Advantageous Effects of Invention

According to the present invention, it is possible to easily identify, based on measurement data before and after maintenance, a type of feature and feature detection algorithm that allow a difference between before and after maintenance to be optimally expressed and select the optimal combination from among a plurality of combination candidates of features, feature detection algorithms, and diagnosis processing algorithms with consideration given to detection performance, hardware limits, cost, and the like.

