An abnormality determination system, comprising: a state quantity obtainer configured to obtain a state quantity associated with a mechanical system; and an abnormality determiner configured to, using a learning content obtained in a machine learning process and based on the state quantity, make a determination as to at least one of: whether the mechanical system has an abnormality; an occurrence position at which the abnormality is occurring; and a cause of the abnormality, wherein the mechanical system comprises a motor, a motor control apparatus configured to control driving of the motor, and a motor-driven mechanism drivable by the motor, and wherein the state quantity obtainer comprises a time-series detection data obtainer configured to obtain, as the state quantity, time-series detection data associated with input and output of the motor that is being driven, characterized in that the abnormality determiner comprises an equipment characteristic estimator configured to estimate an equipment characteristic estimated value of the mechanical system based on the time-series detection data wherein the equipment characteristic is at least one of a friction coefficient, resonance frequency, coulomb friction coefficient, viscosity friction coefficient, and inertia, a data abnormality determiner configured to make a determination as to whether the time-series detection data has a data abnormality, and an equipment abnormality determiner configured to, based on the determination made by the data abnormality determiner, make a determination as to whether the motor, the motor control apparatus, and/or the motor-driven mechanism have an equipment abnormality, wherein the equipment abnormality determiner comprises an equipment abnormality cause estimator configured to estimate a cause of the equipment abnormality wherein the equipment abnormality cause estimator is configured to, based on the learning content obtained in the machine learning process, estimate the cause of the equipment abnormality using the equipment characteristic estimated value. a state quantity obtainer configured to obtain a state quantity associated with a mechanical system; and an abnormality determiner configured to, using a learning content obtained in a machine learning process and based on the state quantity, make a determination as to at least one of: whether the mechanical system has an abnormality; an occurrence position at which the abnormality is occurring; and a cause of the abnormality, wherein the mechanical system comprises a motor, a motor control apparatus configured to control driving of the motor, and a motor-driven mechanism drivable by the motor, and wherein the state quantity obtainer comprises a time-series detection data obtainer configured to obtain, as the state quantity, time-series detection data associated with input and output of the motor that is being driven, characterized in that the abnormality determiner comprises an equipment characteristic estimator configured to estimate an equipment characteristic estimated value of the mechanical system based on the time-series detection data wherein the equipment characteristic is at least one of a friction coefficient, resonance frequency, coulomb friction coefficient, viscosity friction coefficient, and inertia, a data abnormality determiner configured to make a determination as to whether the time-series detection data has a data abnormality, and an equipment abnormality determiner configured to, based on the determination made by the data abnormality determiner, make a determination as to whether the motor, the motor control apparatus, and/or the motor-driven mechanism have an equipment abnormality, wherein the equipment abnormality determiner comprises an equipment abnormality cause estimator configured to estimate a cause of the equipment abnormality wherein the equipment abnormality cause estimator is configured to, based on the learning content obtained in the machine learning process, estimate the cause of the equipment abnormality using the equipment characteristic estimated value.