Vibration signal is an important prerequisite for mechanical fault detection.However,early stage defect of rotating machineries is difficult to identify because their incipient energy is interfered with background noises.Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction.However,the existing predetermined multiwavelet bases are independent of the dynamic response signals.In this paper,a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet(MOAMW)is proposed for enhancing the extracting performance of fault symptom.It is able to derive an optimal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm.In this technique,two-scale similarity transform(TST)and symmetric lifting(SymLift)scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective.TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of multiwavelet,respectively.Moreover,the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing resolution during the decomposition process.The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element bearing with an outer race scrape and a gearbox with rub fault. Vibration signal is an important prerequisite for mechanical fault detection. However, early stage defect of rotating machineries is difficult to identify because their incipient energy is interfered with background noises. Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction. However, the existing predetermined multiwavelet bases are independent of the dynamic response signals. This paper, a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelets (MOAMW) is proposed for enhancing the extracting performance of fault symptom. It is able to derive an optimal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm. This technique, two-scale similarity transform (TST) and symmetric lifting (SymLift) scheme are combined to gain high building freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective.TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of multiwavelet, respectively. Moreover, the beneficial feature of the MOAWM lies in that maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing resolution during the decomposition process. the effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element bearing with an outer race scrape and a gearbox with rub fault.