A tool diagnostic device includes a data acquisition unit configured to acquire time-series data related to a deterioration state of a drilling tool when a hole is machined, a diagnostic section extraction unit configured to extract diagnostic section time-series data acquired when machining is performed in a diagnostic section from a middle position to a machining end position of the hole from the time-series data acquired by the data acquisition unit, and a deterioration diagnostic unit configured to diagnose deterioration of the drilling tool using the diagnostic section time-series data extracted by the diagnostic section extraction unit.

The invention relates to a method for calculating the strength and the service life of a process apparatus through which fluid flows, wherein: temperatures existing at a plurality of different points of the apparatus are measured at a first time point in order to obtain temperature measurement values (201); the temperature measurement values are used as constraints in a finite element method (203) in order to determine mechanical stresses existing at a plurality of different points in the material of the apparatus as stress values (204); the remaining service life of the material of the apparatus is determined from the obtained stress values (205); the remaining service life of the material of the apparatus is determined also in dependence on data regarding the apparatus that were determined at a second time point (207), which second time point is earlier than the first time point.

The disclosure is provided to transmit a sensor value from a sensor to an external device with high efficiency. A sensor value of a sensor is acquired, basic data as time-series data is generated with reference to the acquired sensor value, differential data indicating a difference between the basic data and measurement data as time-series data corresponding to the sensor value acquired from the sensor is generated, and the differential data is transmitted to an external device through wireless communication.

A time series data monitoring system includes: a series pattern candidate generating unit that generates series pattern candidates included in time series data obtained from a monitored system using the time series data and a prediction model of the time series data; and a series pattern generating unit that classifies the series pattern candidates generated by the series pattern candidate generating unit and outputs, as a series pattern of the time series data, a candidate satisfying a predetermined condition among the classified series pattern candidates.

System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data. System predicts enterprise system response time by using trained predictive model, input time series data or lag between input time series data and historical output time series data.

The disclosure is provided to transmit a sensor value from a sensor to an external device with high efficiency. A sensor value of a sensor is acquired, basic data as time-series data is generated with reference to the acquired sensor value, differential data indicating a difference between the basic data and measurement data as time-series data corresponding to the sensor value acquired from the sensor is generated, and the differential data is transmitted to an external device through wireless communication.

In the present invention signals are obtained from a plurality of sensors S.sub.1, S.sub.2, Sn and fed into an encoder (12). The encoder (12) is operable in use to receive input signals from each of the sensors S.sub.1, S.sub.2, S.sub.n and translate said signals into one or more vectors characterising the state of one or more of the operational parameters of the monitored system, hereinafter referred to as an encoded vector V.sub.E. The signals from the sensors S.sub.1, S.sub.2, S.sub.n may relate to one or more different operational parameters of a connected system. The encoded vector V.sub.E is fed into a translation engine 13 which translates the encoded vector V.sub.E into feature space to form a feature vector V.sub.F. The feature vector V.sub.F is subsequently fed into a residual vector generator (16) which compares the feature vector V.sub.F with a predicted vector V.sub.P generated by a prediction engine (14) and thereby output a residual vector V.sub.R which characterises any differences between the feature vector V.sub.F and the predicted vector V.sub.P, In addition, the feature vector V.sub.F is also fed directly into the prediction engine (14). In this way, the current operational state of each of the variable parameters of the monitored system can be input into the prediction engine (14) to update subsequent predictions made by the prediction engine (14). The formed residual vector V.sub.R is then input into a computation unit (18) for analysis, such as to determine whether the differences identified between the predicted and feature vectors V.sub.P, V.sub.F indicate that there is a fault in the monitored system.

System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data. System predicts enterprise system response time by using trained predictive model, input time series data or lag between input time series data and historical output time series data.