Time-frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with signals having a strong non-stationary characteristic or multi-component signals having close frequency components. To overcome this shortcoming, a new TFA technique applying a polynomial chirplet transform (PCT) in association with a synchroextracting transform (SET) is proposed in this study. It is shown that the energy concentration of the timefrequency representation (TFR) of a strong frequency modulated signal from a PCT transform can be further enhanced by a SET transform. The technique can also be employed to accurately extract the signal components of a multi-component nonstationary signal with close frequency components by adopting an iterative process. It is found that the TFR calculated from the proposed technique matches well with the ideal TFR which demonstrates the superiority of the current technique in dealing with non-stationary signals having rapidly changing dynamics. Results from the analysis of the experimental data under varying speed conditions confirm the validity of the proposed technique in dealing with non-stationary signals from practical sources.

In current research works, a number of intelligent fault diagnosis methods have been proposed with the assistance of domain adaptation approach, which attempt to distinguish the health modes for target domain data using the diagnostic knowledge learned from source domain data. An important assumption for these methods is that the label information for the source domain data should be known in advance. However, the high-quality condition monitoring data with sufficient label information is difficult to be acquired in the actual field, which can greatly hinder the effectiveness of domain adaptation–based fault diagnosis methods. The simulation model of the rotating machine is an effective approach to provide an insight into the characteristics of the mechanical equipment, which can also easily carry the sufficient label information for the mechanical equipment under various operating conditions. In this study, a simulation data–driven domain adaptation approach is proposed for the intelligent fault diagnosis of mechanical equipment. The simulation data from a rotor-bearing system are used to build the source domain data set, and the diagnostic knowledge learned from the simulation data is used to realize the healthy mode identification of mechanical equipment in the actual field. The proposed domain adaption approach consists of two parts. The first part is to achieve the conditional distribution alignment between source domain data and target domain supervised data in an alternative way. The second part is to achieve the marginal distribution alignment between source domain data and target domain unsupervised data in an adversarial training process. The proposed domain adaptation method is evaluated on two case studies, the diagnostic results on two case studies indicate that the proposed domain adaptation method is capable of realizing the fault diagnosis of mechanical equipment using the diagnostic knowledge learned from simulation data.