Method for detecting a malfunction suffered by an integrated drive generator, referred to as IDG to be monitored, in an aircraft, the method being implemented by a data analysis system comprising an artificial intelligence of the machine learning type, characterized in that the method comprises the following steps: - collecting reference data relating to measurements made and recorded during previous flights of aircraft equipped with IDGs, called reference IDGs, the reference data comprising at least measurements corresponding to the following parameters, for each reference IDG: - Flow of fuel supplying a jet engine with which the reference IDG in question is associated; - Position of a fuel return valve associated with the reference IDG in question; - Temperature measured by a sensor on the inlet port of the reference IDG in question; - Temperature measured by a sensor on the outlet port of the reference IDG in question; - Frequency of electrical signal produced by the reference IDG in question; - Temperature of oil supplying the jet engine with which the reference IDG in question is associated; - carrying out a training, in supervisor mode, of the artificial intelligence of the machine learning type by virtue of the reference data, with a first classification run for training to detect potential malfunctions suffered by IDGs and a second classification run for training to determine causes of the malfunctions where relevant; - using in production the artificial intelligence of the machine learning type after validation of the training; - collecting data to be analysed, of the same type as the reference data, relating to measurements made and recorded during flights of the aircraft comprising the IDG to be monitored; - using the artificial intelligence of the machine learning type that has been trained, and thus predicting a potential malfunction suffered by the IDG to be monitored and predicting, where relevant, a cause of the malfunctions by virtue, respectively, of the said first and second runs, using the data to be analysed. - collecting reference data relating to measurements made and recorded during previous flights of aircraft equipped with IDGs, called reference IDGs, the reference data comprising at least measurements corresponding to the following parameters, for each reference IDG: - Flow of fuel supplying a jet engine with which the reference IDG in question is associated; - Position of a fuel return valve associated with the reference IDG in question; - Temperature measured by a sensor on the inlet port of the reference IDG in question; - Temperature measured by a sensor on the outlet port of the reference IDG in question; - Frequency of electrical signal produced by the reference IDG in question; - Temperature of oil supplying the jet engine with which the reference IDG in question is associated; - carrying out a training, in supervisor mode, of the artificial intelligence of the machine learning type by virtue of the reference data, with a first classification run for training to detect potential malfunctions suffered by IDGs and a second classification run for training to determine causes of the malfunctions where relevant; - using in production the artificial intelligence of the machine learning type after validation of the training; - collecting data to be analysed, of the same type as the reference data, relating to measurements made and recorded during flights of the aircraft comprising the IDG to be monitored; - using the artificial intelligence of the machine learning type that has been trained, and thus predicting a potential malfunction suffered by the IDG to be monitored and predicting, where relevant, a cause of the malfunctions by virtue, respectively, of the said first and second runs, using the data to be analysed. - Flow of fuel supplying a jet engine with which the reference IDG in question is associated; - Position of a fuel return valve associated with the reference IDG in question; - Temperature measured by a sensor on the inlet port of the reference IDG in question; - Temperature measured by a sensor on the outlet port of the reference IDG in question; - Frequency of electrical signal produced by the reference IDG in question; - Temperature of oil supplying the jet engine with which the reference IDG in question is associated; - carrying out a training, in supervisor mode, of the artificial intelligence of the machine learning type by virtue of the reference data, with a first classification run for training to detect potential malfunctions suffered by IDGs and a second classification run for training to determine causes of the malfunctions where relevant; - using in production the artificial intelligence of the machine learning type after validation of the training; - collecting data to be analysed, of the same type as the reference data, relating to measurements made and recorded during flights of the aircraft comprising the IDG to be monitored; - using the artificial intelligence of the machine learning type that has been trained, and thus predicting a potential malfunction suffered by the IDG to be monitored and predicting, where relevant, a cause of the malfunctions by virtue, respectively, of the said first and second runs, using the data to be analysed.