A complex machine tool having a failure prediction function comprising: a spindle (10, 10A,10B) adapted to hold and rotate a workpiece; a vibration part that is a failure prediction target part being a rotation mechanism section such as a bearing or a ball screw; a tool spindle adapted to hold a tool that processes the workpiece; and a sensor unit comprising a sensor attached to the vibration part, and a load detection sensor adapted to detect that the workpiece has been started to be processed based on a change in a spindle load; wherein the complex machine tool includes: an FFT analysis unit adapted to perform frequency analysis on sensor information acquired by the sensor while the workpiece held by the spindle (10, 10A, 10B) is not being processed by the tool and while the vibration part is vibrated by the spindle (10, 10A, 10B) as a vibrating source in a constant speed state; a machine learning unit adapted to acquire an amplitude spectrum of a frequency band of interest obtained by the frequency analysis performed by the FFT analysis unit, and calculate an average amplitude spectrum of the frequency band of interest based on a learned leaning model; and a comparison unit adapted to compare the amplitude spectrum of the frequency band of interest obtained by the FFT analysis unit with the average amplitude spectrum of the frequency band of interest obtained by the machine learning unit. a spindle (10, 10A,10B) adapted to hold and rotate a workpiece; a vibration part that is a failure prediction target part being a rotation mechanism section such as a bearing or a ball screw; a tool spindle adapted to hold a tool that processes the workpiece; and a sensor unit comprising a sensor attached to the vibration part, and a load detection sensor adapted to detect that the workpiece has been started to be processed based on a change in a spindle load; wherein the complex machine tool includes: an FFT analysis unit adapted to perform frequency analysis on sensor information acquired by the sensor while the workpiece held by the spindle (10, 10A, 10B) is not being processed by the tool and while the vibration part is vibrated by the spindle (10, 10A, 10B) as a vibrating source in a constant speed state; a machine learning unit adapted to acquire an amplitude spectrum of a frequency band of interest obtained by the frequency analysis performed by the FFT analysis unit, and calculate an average amplitude spectrum of the frequency band of interest based on a learned leaning model; and a comparison unit adapted to compare the amplitude spectrum of the frequency band of interest obtained by the FFT analysis unit with the average amplitude spectrum of the frequency band of interest obtained by the machine learning unit. an FFT analysis unit adapted to perform frequency analysis on sensor information acquired by the sensor while the workpiece held by the spindle (10, 10A, 10B) is not being processed by the tool and while the vibration part is vibrated by the spindle (10, 10A, 10B) as a vibrating source in a constant speed state; a machine learning unit adapted to acquire an amplitude spectrum of a frequency band of interest obtained by the frequency analysis performed by the FFT analysis unit, and calculate an average amplitude spectrum of the frequency band of interest based on a learned leaning model; and a comparison unit adapted to compare the amplitude spectrum of the frequency band of interest obtained by the FFT analysis unit with the average amplitude spectrum of the frequency band of interest obtained by the machine learning unit.