A diagnosis device stores a model used for diagnosing the condition of an industrial machine in a storage unit, acquires data related to the condition of the industrial machine, and based on the acquired data, determines the condition of the industrial machine by using the model stored in the storage unit. Then, in response to detecting that a component of the industrial machine has been replaced based on the acquired data and the data related to the determined condition of the industrial machine, the diagnosis device adapts the model stored in the storage unit to the condition of the industrial machine whose component has been replaced.

A manufacturing process monitoring apparatus capable of determining a manufacturing process is anomaly, without requiring any threshold value for determining the as anomaly is provided. The manufacturing process monitoring apparatus includes a data conversion unit configured to convert process data of a manufacturing facility, a feature value analysis unit configured to analyze the converted data based on information on feature values, a data restoration unit configured to restore data for each of a plurality of categories based on the information on the feature values and information on the analyzed result, a similarity calculation unit configured to calculate a similarity for each of the plurality of categories based on the data used when being analyzed and the restored data, a category determination unit configured to determine a category of the data based on the similarity for each of the plurality of categories, a category classification unit configured to classify the category to which the process data belongs, and a process state diagnostic unit configured to diagnose a state of the manufacturing process based on a result of comparison between the determined category and the classified category.

A turning thread automatic diagnosing method includes collecting a spindle actual speed and a feeding shaft actual speed of a lathe; based upon the spindle actual speed and the feeding shaft actual speed, continuously calculating thread errors to obtain a thread error curve; and analyzing an error cause according to the thread error curve. In addition, a turning thread automatic diagnosing system is disclosed, using the method. A data processing system and a storage medium are also disclosed. The technology of the embodiments can automatically identify the problem causing a thread error and provide a corresponding solution.

The present invention quickly determines causes of shape errors, machined surface defects, etc. by comparing the machined surface shape calculated on the basis of the actual motor position, and the machined surface shape obtained by actually measuring the machined surface of a machined workpiece. This determining system is equipped with: a motor position acquisition unit for acquiring the actual position of a motor for driving the drive shaft of a machine tool; a tool information acquisition unit for acquiring tool information which includes the machine tool driveshaft configuration, the instrument shape and the unmachined workpiece shape; a motor position machined surface calculation unit for calculating the shape of the machined surface of the machined workpiece on the basis of the tool information and the actual position of the motor; an actual machined surface acquisition unit for acquiring the shape of the machined surface of an actually machined workpiece; and a machined surface analysis unit for comparing a first correlation, which is the correlation between the shape of the machined surface calculated by the motor position machined surface calculation unit and the shape of the machined surface acquired by the actual machined surface acquisition unit.

An information processing apparatus detects an abnormality sign in a semiconductor manufacturing apparatus. The apparatus includes: a sensor data collector configured to acquire sensor waveform data with respect to a sensor value axis and a time axis measured by a semiconductor manufacturing apparatus that is executing a process according to a same recipe; a monitoring band calculator configured to calculate each monitoring band for the sensor value axis and the time axis used in a waveform monitoring method from a predetermined number or more of the sensor waveform data; and an abnormality sign detector configured to monitor a waveform of the sensor waveform data using each monitoring band for the sensor value axis and the time axis and detect an abnormality sign of the semiconductor manufacturing apparatus.

A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).

A method for diagnosing faults in an industrial machine. The method comprises identifying a first state associated with the industrial machine, identifying at least one second state associated with the industrial machine based on the first state, for each identified second state, determining a state time associated with the second state, determining a fault indicator condition responsive to determining, based on the at least one second state and the respective state times, that the industrial machine spent a time period different to a predetermined time period in at least one of the at least one second states, generating, responsive to determining the fault indication, diagnostic information comprising an indication of the one of the at least one second state, and outputting the diagnostic information in a user interface of the industrial machine.

A turning thread automatic diagnosing method includes collecting a spindle actual speed and a feeding shaft actual speed of a lathe; based upon the spindle actual speed and the feeding shaft actual speed, continuously calculating thread errors to obtain a thread error curve; and analyzing an error cause according to the thread error curve. In addition, a turning thread automatic diagnosing system is disclosed, using the method. A data processing system and a storage medium are also disclosed. The technology of the embodiments can automatically identify the problem causing a thread error and provide a corresponding solution.

A state identification device includes: a feature amount extraction unit that extracts a feature amount from physical data of a machine tool unit which shifts to a plurality of operation states; a feature amount storage unit in which a region is provided as a storage destination of the extracted feature amount for each of the plurality of operation states to which the machine tool unit shifts; a similarity calculation unit that calculates a similarity between the extracted feature amount and a feature amount stored in the feature amount storage unit; a state determination unit that determines an operation state of the machine tool unit based on the calculated similarity; and a storage destination determination unit that determines the region serving as the storage destination of the extracted feature amount based on a determination result of the state determination unit.

A manufacturing process monitoring apparatus capable of determining a manufacturing process is anomaly, without requiring any threshold value for determining the as anomaly is provided. The manufacturing process monitoring apparatus includes a data conversion unit configured to convert process data of a manufacturing facility, a feature value analysis unit configured to analyze the converted data based on information on feature values, a data restoration unit configured to restore data for each of a plurality of categories based on the information on the feature values and information on the analyzed result, a similarity calculation unit configured to calculate a similarity for each of the plurality of categories based on the data used when being analyzed and the restored data, a category determination unit configured to determine a category of the data based on the similarity for each of the plurality of categories, a category classification unit configured to classify the category to which the process data belongs, and a process state diagnostic unit configured to diagnose a state of the manufacturing process based on a result of comparison between the determined category and the classified category.