Abstract: A friction stir welding apparatus includes a welding tool that includes a shoulder and a probe supported by the shoulder, is inserted into a plurality of welding target members, and moves while rotating to weld the plurality of welding target members, a spindle motor that is coupled to the welding tool to rotate the welding tool in a predetermined direction, a welding head that supports the spindle motor, and an apparatus body that supports the welding head, applies a drive signal to the spindle motor, and moves the welding tool along a welding line while rotating the welding tool.

Abstract: A friction stir welding apparatus includes a welding tool that includes a shoulder and a probe supported by the shoulder, is inserted into a plurality of welding target members, and moves while rotating to weld the plurality of welding target members, a spindle motor that is coupled to the welding tool to rotate the welding tool in a predetermined direction, a welding head that supports the spindle motor, and an apparatus body that supports the welding head, applies a drive signal to the spindle motor, and moves the welding tool along a welding line while rotating the welding tool.

Abstract: A phenomenon pattern extraction unit extracts a phenomenon pattern of a past sensor signal of a facility. A related information correlation unit correlates the sensor signal based on maintenance history information. A phenomenon pattern classification reference creation unit creates a classification reference for classifying a phenomenon pattern based on the extracted phenomenon pattern and a work keyword included in the maintenance history information correlated with the sensor signal as the source of the phenomenon pattern. A phenomenon pattern classification unit classifies the phenomenon pattern based on the classification reference. A diagnosis model creation unit creates a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the classified phenomenon pattern and the work keyword.

Abstract: An abnormality predictor diagnosis system includes: a sensor data acquisition means that acquires sensor data including a detection value of a sensor installed in a mechanical facility; a learning means that sets a learning target of sensor data in a period in which the mechanical facility is known to be normal, and learns a time-series waveform of the sensor data as a normal model; and a diagnosis means that diagnoses the mechanical facility for the presence of an abnormality predictor based on comparison between the normal model and the time-series waveform of the sensor data of a diagnosis target. The abnormality predictor diagnosis system can diagnose the mechanical facility for the presence of an abnormality predictor with high accuracy.

Abstract: An objective is to identify the health state of mechanical equipment and provide information usable for determining maintenance work timing or the like. A health management system includes a time-series data acquisition unit configured to acquire multi-dimensional sensor data and environmental data from mechanical equipment; a first discrimination unit configured to quantify the equipment state of the mechanical equipment by a statistical method using normal data as learning data; a second discrimination unit configured to quantify the health state indicating the performance or quality of the mechanical equipment by a statistical method using normal data; and an output unit configured to display and/or output to the outside the quantified equipment state and health state.

Abstract: A phenomenon pattern extraction unit extracts a phenomenon pattern of a past sensor signal of a facility. A related information correlation unit correlates the sensor signal based on maintenance history information. A phenomenon pattern classification reference creation unit creates a classification reference for classifying a phenomenon pattern based on the extracted phenomenon pattern and a work keyword included in the maintenance history information correlated with the sensor signal as the source of the phenomenon pattern. A phenomenon pattern classification unit classifies the phenomenon pattern based on the classification reference. A diagnosis model creation unit creates a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the classified phenomenon pattern and the work keyword.

Abstract: An objective is to identify the health state of mechanical equipment and provide information usable for determining maintenance work timing or the like. A health management system includes a time-series data acquisition unit configured to acquire multi-dimensional sensor data and environmental data from mechanical equipment; a first discrimination unit configured to quantify the equipment state of the mechanical equipment by a statistical method using normal data as learning data; a second discrimination unit configured to quantify the health state indicating the performance or quality of the mechanical equipment by a statistical method using normal data; and an output unit configured to display and/or output to the outside the quantified equipment state and health state.