An abnormality predictor diagnosis system comprising: a sensor data acquisition means configured to acquire sensor data including a detection value of a sensor installed in a mechanical facility in which a predetermined operation process is repeated; a learning means configured to set a learning target of a time-series waveform of the sensor data in a period in which the mechanical facility is known to be normal, extract therefrom a start point of the waveform at a start time of a respective operation process, a plurality of extremum points including a local maximum point and a local minimum point of the waveform, and an end point of the waveform at an end time of the operation process as feature points, acquire the detection values of the sensor at the feature points and elapsed times from the start time of the operation process to each of the feature points, as normal waveform data indicating the time-series waveform of the sensor data for the operation process repeated, convert the normal waveform data to a group of feature vectors, and cluster each of the feature vectors as a normal model; and a diagnosis means configured to set a diagnosis target of the time-series waveform of the sensor data, extract therefrom the start point of the waveform at the start time of a respective operation process, the plurality of extremum points including the local maximum point and the local minimum point of the waveform, and the end point of the waveform at the end time of the operation process as feature points, acquire the detection values of the sensor at the feature points and the elapsed times from the start time of the operation process and to each of the feature points, as waveform data indicating the waveform, and diagnose the mechanical facility for presence of an abnormality predictor based on comparison between the waveform data and the normal model, characterised in that the learning means is configured to learn at least one cluster, represented by a cluster center and a cluster radius, as the normal model by clustering a feature vector having components which are obtained by performing normalization processing on the detection value and the elapsed time included in the waveform data as the learning target for producing dimensionless quantities which allow mutual comparison, and the diagnosis means is configured to perform normalization processing on the waveform data set as the diagnosis target to convert to a feature vector, identify a cluster with the cluster center closest to the feature vector among the at least one cluster, calculate a ratio of a distance between the cluster center and the feature vector to the cluster radius as an abnormality measure, and diagnose the mechanical facility for presence of an abnormality predictor based on the abnormality measure. a sensor data acquisition means configured to acquire sensor data including a detection value of a sensor installed in a mechanical facility in which a predetermined operation process is repeated; a learning means configured to set a learning target of a time-series waveform of the sensor data in a period in which the mechanical facility is known to be normal, extract therefrom a start point of the waveform at a start time of a respective operation process, a plurality of extremum points including a local maximum point and a local minimum point of the waveform, and an end point of the waveform at an end time of the operation process as feature points, acquire the detection values of the sensor at the feature points and elapsed times from the start time of the operation process to each of the feature points, as normal waveform data indicating the time-series waveform of the sensor data for the operation process repeated, convert the normal waveform data to a group of feature vectors, and cluster each of the feature vectors as a normal model; and a diagnosis means configured to set a diagnosis target of the time-series waveform of the sensor data, extract therefrom the start point of the waveform at the start time of a respective operation process, the plurality of extremum points including the local maximum point and the local minimum point of the waveform, and the end point of the waveform at the end time of the operation process as feature points, acquire the detection values of the sensor at the feature points and the elapsed times from the start time of the operation process and to each of the feature points, as waveform data indicating the waveform, and diagnose the mechanical facility for presence of an abnormality predictor based on comparison between the waveform data and the normal model, characterised in that the learning means is configured to learn at least one cluster, represented by a cluster center and a cluster radius, as the normal model by clustering a feature vector having components which are obtained by performing normalization processing on the detection value and the elapsed time included in the waveform data as the learning target for producing dimensionless quantities which allow mutual comparison, and the diagnosis means is configured to perform normalization processing on the waveform data set as the diagnosis target to convert to a feature vector, identify a cluster with the cluster center closest to the feature vector among the at least one cluster, calculate a ratio of a distance between the cluster center and the feature vector to the cluster radius as an abnormality measure, and diagnose the mechanical facility for presence of an abnormality predictor based on the abnormality measure.