An abnormality detector includes a signal output unit for detecting a sign of an abnormality based on a physical quantity acquired from a manufacturing machine and outputting a signal; and a machine learning device including state observation unit for observing, as a state variable representing a present state of the environment, physical quantity data indicating the physical quantity related to an operation of the manufacturing machine from the manufacturing machine; a label data acquisition unit for acquiring, as label data, operation state data indicating an operation state of the manufacturing machine; a learning unit for learning the operation state of the manufacturing machine with respect to the physical quantity, using the state variable and the label data; and an estimation result output unit for estimating the operation state of the manufacturing machine using a learning result by the learning unit and outputting an estimation result.

A plant operation data monitoring device comprises: an input section that receives operation data on a plant; and a calculator that includes databases storing the operation data received, and a computing section executing a program. The computing section stores the operation data received in a first database of the databases in time series. The computing section determines from peak values of the operation data stored whether gradients of the operation data are positive or negative, and then stores the gradients in a second database of the databases for positive gradients or in the second database of the databases for negative gradients in time series. The computing section determines threshold values for abnormality determination about the positive and negative gradients, divides the positive gradients and the negative gradients into normal values and abnormal values, and additionally stores the divided gradients in the second database for the positive or negative gradients.

A massage machine includes a main body that includes a treatment portion configured to perform treatment with respect to a user; a detecting portion configured to detect a running state of a component of the main body and to obtain running state data; a storage portion configured to store the running state data; and a control unit configured to control an operation of the main body. The control unit includes: a determining portion configured to determine presence or absence of an abnormality related to the main body; and an abnormality outputting portion configured to output an information related to the abnormality and the running state data when the abnormality determining portion determines that the abnormality is present.

An abnormality determining apparatus for a motor driven mechanism includes circuitry. The circuitry is configured to acquire time-series data with respect to an input to and an output from a motor which drives the motor driven mechanism, detect data abnormality in the time-series data, and determine, based on the data abnormality, whether mechanical abnormality in the motor driven mechanism occurs.

A remote unit (1) that controls a control target (4) on the basis of a command from a CPU unit (2) is provided with: an external input section (11) to receive a detection result of a state of the control target from a detection means (3) that detects the state of the control target; an output section (13) to output a control output for controlling the control target; and an abnormality determining section (12) to determine an abnormality of the control target on the basis of the detection result and to output to the output section, a control instruction to instruct a change or a stop of the control output if the control target is determined to be abnormal.

An abnormality score calculation apparatus according to an embodiment includes a processing circuit configured to: acquire first data concerning a status of a product or a manufacturing process; calculate based on the first data an abnormality score for a respective one of a plurality of abnormality modes or for a respective one of a plurality of pieces of the first data of various types; and convert a scale of a respective one of a plurality of abnormality scores including the abnormality score in such a manner that the abnormality scores become substantially equal in occurrence degree.

An abnormality score calculation apparatus according to an embodiment includes a processing circuit configured to: acquire first data concerning a status of a product or a manufacturing process; calculate based on the first data an abnormality score for a respective one of a plurality of abnormality modes or for a respective one of a plurality of pieces of the first data of various types; and convert a scale of a respective one of a plurality of abnormality scores including the abnormality score in such a manner that the abnormality scores become substantially equal in occurrence degree.

A plant operation data monitoring device comprises: an input section that receives operation data on a plant; and a calculator that includes databases storing the operation data received, and a computing section executing a program. The computing section stores the operation data received in a first database of the databases in time series. The computing section determines from peak values of the operation data stored whether gradients of the operation data are positive or negative, and then stores the gradients in a second database of the databases for positive gradients or in the second database of the databases for negative gradients in time series. The computing section determines threshold values for abnormality determination about the positive and negative gradients, divides the positive gradients and the negative gradients into normal values and abnormal values, and additionally stores the divided gradients in the second database for the positive or negative gradients.

A numerical controller that detects occurrence of an abnormality according to a neighborhood method includes a sampling value acquisition unit configured to collect sampling values indicative of a state of a machine or environment, wherein the sampling values are collected during normal machining and during operation; a learning unit configured to generate a set of the sampling values during the normal machining; and an abnormality degree determination unit configured to compute an abnormality degree on the basis of a distance between the sampling value during the operation and the set of the sampling values during the normal machining.

An abnormality detection system, support device, and model generation method for generating a more highly accurate abnormality detection model before an actual operation are provided. A model generation part includes a section for generating feature values from state values provided from a state value storage part; a section for calculating importance levels respectively for the generated feature values based on plural methods, wherein the importance levels indicating a degree that is effective for abnormality detection; and a section for integrating the importance levels calculated based on the plural methods for each of the generated feature values and determining rankings of the importance levels of the generated feature values.