A method for detecting a structural fault of an electric motor, comprising the steps: (i) acquiring, during an acquisition window of predetermined duration, a measurement signal of a physical parameter representative of the rotation of the electric motor, at a predefined sampling frequency (f), (ii) obtaining the high frequency component of the measurement signal, resulting in a corrected signal (S_cor), (iii) Applying a set of band-pass filters (F-1,..., F-n) to the corrected signal (S_cor), each band-pass filter (F-1,..., F-n) having a central frequency (c-1,..., c-n) and a bandwidth (q-1,..., q-n), the central frequencies of the band-pass filters (F-1,..., F-n) being in ascending order and the bandwidth (q-1,..., q-n) being separated from each other, each band-pass filter (F-1,..., F-n) having an index (1,..., n) corresponding to the rank of the central frequency in a sorted list of the central frequencies (c-1,..., c-n), resulting in a set of filtered corrected signals (FS_cor-1,..., FS_cor-n), (iv) Determining a stator frequency Fs and a rotor frequency Fr of the electric motor, (v) Computing a frequency signature vector comprising a plurality of components (FSV 1,..., FSV p), each component (FSV k) of the frequency signature vector being a predefined linear combination of the stator frequency (Fs) and of the rotor frequency (Fr), from the equation: FSV k = a k * Fs + b k * Fr, the coefficients a k, b k being predefined integers, characterised by (vi) Computing a temporal symptom vector from the computed frequency signature vector and from the set of filtered corrected signals (FS_cor-1,..., FS_cor-n), the temporal symptom vector comprising a plurality of components (TSV 1,..., TSV p), the computation comprising the sub-steps: (vi-1) For each time sample (t i) of the acquisition window and each component (FSV k) of the frequency signature vector, selecting the band-pass filter (F-r) of central frequency (c-r) which is closest to the value (FSV k) for time sample (t i), (vi-2) Affecting the output value of the selected band-pass filter (F-r) to the component number k (TSV k) of the temporal symptom vector for time sample (t i), (vi-3) Iterating steps (vi-1) and (vi-2) for all time samples of the acquisition window, resulting in a temporal symptom vector, (vii) Detecting a structural fault of the electric motor from the determined temporal symptom vector and from a classifier model. (i) acquiring, during an acquisition window of predetermined duration, a measurement signal of a physical parameter representative of the rotation of the electric motor, at a predefined sampling frequency (f), (ii) obtaining the high frequency component of the measurement signal, resulting in a corrected signal (S_cor), (iii) Applying a set of band-pass filters (F-1,..., F-n) to the corrected signal (S_cor), each band-pass filter (F-1,..., F-n) having a central frequency (c-1,..., c-n) and a bandwidth (q-1,..., q-n), the central frequencies of the band-pass filters (F-1,..., F-n) being in ascending order and the bandwidth (q-1,..., q-n) being separated from each other, each band-pass filter (F-1,..., F-n) having an index (1,..., n) corresponding to the rank of the central frequency in a sorted list of the central frequencies (c-1,..., c-n), resulting in a set of filtered corrected signals (FS_cor-1,..., FS_cor-n), (iv) Determining a stator frequency Fs and a rotor frequency Fr of the electric motor, (v) Computing a frequency signature vector comprising a plurality of components (FSV 1,..., FSV p), each component (FSV k) of the frequency signature vector being a predefined linear combination of the stator frequency (Fs) and of the rotor frequency (Fr), from the equation: FSV k = a k * Fs + b k * Fr, the coefficients a k, b k being predefined integers, characterised by (vi) Computing a temporal symptom vector from the computed frequency signature vector and from the set of filtered corrected signals (FS_cor-1,..., FS_cor-n), the temporal symptom vector comprising a plurality of components (TSV 1,..., TSV p), the computation comprising the sub-steps: (vi-1) For each time sample (t i) of the acquisition window and each component (FSV k) of the frequency signature vector, selecting the band-pass filter (F-r) of central frequency (c-r) which is closest to the value (FSV k) for time sample (t i), (vi-2) Affecting the output value of the selected band-pass filter (F-r) to the component number k (TSV k) of the temporal symptom vector for time sample (t i), (vi-3) Iterating steps (vi-1) and (vi-2) for all time samples of the acquisition window, resulting in a temporal symptom vector, (vi-1) For each time sample (t i) of the acquisition window and each component (FSV k) of the frequency signature vector, selecting the band-pass filter (F-r) of central frequency (c-r) which is closest to the value (FSV k) for time sample (t i), (vi-2) Affecting the output value of the selected band-pass filter (F-r) to the component number k (TSV k) of the temporal symptom vector for time sample (t i), (vi-3) Iterating steps (vi-1) and (vi-2) for all time samples of the acquisition window, (vii) Detecting a structural fault of the electric motor from the determined temporal symptom vector and from a classifier model.