A wind turbine generator fault detection method is described. The method includes obtaining a first signal from a generator of a wind turbine and a second signal from a vibration sensor coupled to the wind turbine, the first signal representing an output current of the generator, and the second signal being a time-sampled signal representing vibrations of a bearing in the wind turbine. Determining a shaft rotation frequency signal from the first signal, the shaft rotation frequency signal representing a time-varying rotational speed of a shaft of the wind turbine. Resampling an envelope of the second signal based on the shaft rotation frequency signal to provide a third signal, the third signal being an angular sampled signal. Detecting, by the at least one processor, a fault in the bearing of the wind turbine by identifying a characteristic signature of a bearing fault in the third signal.

A method for monitoring at least one journal bearing for a shaft in respect to at least one acoustic event is provided. The at least one journal bearing is coupled with a gearbox, wherein a time dependent solid borne sound signal is detected by at least one solid borne sound signal sensor, the output signal of the at least one solid borne sound signal sensor is transmitted to a signal pre-processing device, frequency components of the solid borne sound signal with of more than 250 kHz are amplified in the signal pre-processing device and/or with a frequency of less than 50 kHz are attenuated in the signal pre-processing device, and the output signal of the signal pre-processing device is wirelessly transmitted via an antenna device to a signal evaluation device for the detection and/or location of the at least one acoustic event.

A method for monitoring at least one journal bearing for a shaft in respect to at least one acoustic event is provided. The at least one journal bearing is coupled with a gearbox, wherein a time dependent solid borne sound signal is detected by at least one solid borne sound signal sensor, the output signal of the at least one solid borne sound signal sensor is transmitted to a signal pre-processing device, frequency components of the solid borne sound signal with of more than 250 kHz are amplified in the signal pre-processing device and/or with a frequency of less than 50 kHz are attenuated in the signal pre-processing device, and the output signal of the signal pre-processing device is wirelessly transmitted via an antenna device to a signal evaluation device for the detection and/or location of the at least one acoustic event.

Methods and apparatus for operating a rod pumping unit for a wellbore are provided. One example method includes measuring vibration data in the time domain using at least one sensor coupled to a housing for a moving component of the rod pumping unit; converting the vibration data in the time domain to vibration data in the frequency domain; determining that: (1) at least one frequency component of the frequency-domain vibration data in a first frequency band has a power above a first threshold; or (2) a number of frequency components of the frequency-domain vibration data in a second frequency band having power above a second threshold is above a third threshold; and outputting an indication based on the determination.

Methods and apparatus for operating a rod pumping unit for a wellbore are provided. One example method includes measuring vibration data in the time domain using at least one sensor coupled to a housing for a moving component of the rod pumping unit; converting the vibration data in the time domain to vibration data in the frequency domain; determining that: (1) at least one frequency component of the frequency-domain vibration data in a first frequency band has a power above a first threshold; or (2) a number of frequency components of the frequency-domain vibration data in a second frequency band having power above a second threshold is above a third threshold; and outputting an indication based on the determination.

Provided is a system for real-time condition diagnosis of a bearing module including a self-power generation module installed in an outer ring of a bearing to generate electricity using vibration generated from a micro whirling motion of the bearing, a behavior detection unit installed in the bearing to detect behavior information of the bearing in real time, a wireless transmission module connected to the behavior detection unit to transmit the real-time detected behavior information of the bearing to an external device, and a condition diagnosis unit that receives the behavior information of the bearing transmitted from the wireless transmission module and diagnoses condition of the bearing in real time, wherein the real-time behavior information of the behavior detection unit installed in the bearing is transmitted through the electricity obtained by the self-power generation module.

Provided is a system for real-time condition diagnosis of a bearing module including a self-power generation module installed in an outer ring of a bearing to generate electricity using vibration generated from a micro whirling motion of the bearing, a behavior detection unit installed in the bearing to detect behavior information of the bearing in real time, a wireless transmission module connected to the behavior detection unit to transmit the real-time detected behavior information of the bearing to an external device, and a condition diagnosis unit that receives the behavior information of the bearing transmitted from the wireless transmission module and diagnoses condition of the bearing in real time, wherein the real-time behavior information of the behavior detection unit installed in the bearing is transmitted through the electricity obtained by the self-power generation module.

Conformance testing of a rebuilt motor includes disposing a motor under test in a motor test stand, coupling a plurality of sensors to the motor under test including a first vibrational sensor to the motor under test; a first temperature sensor to the motor under test; a first rotational speed sensor to the motor under test. A set of test parameters are received comprising parameters for a conformance test, the test parameters including a vibrational sensor parameter, a temperature sensor parameter, and a rotational speed parameter. A motor under test is placed in an on-state and a processing device receives sensor data simultaneously from the sensors, determines that the rotational speed satisfies the rotational speed parameter, and stores time sampled sensor data received from the plurality of sensors relative to a time zero corresponding to a time when the rotational speed satisfies the rotational speed.

Conformance testing of a rebuilt motor includes disposing a motor under test in a motor test stand, coupling a plurality of sensors to the motor under test including a first vibrational sensor to the motor under test; a first temperature sensor to the motor under test; a first rotational speed sensor to the motor under test. A set of test parameters are received comprising parameters for a conformance test, the test parameters including a vibrational sensor parameter, a temperature sensor parameter, and a rotational speed parameter. A motor under test is placed in an on-state and a processing device receives sensor data simultaneously from the sensors, determines that the rotational speed satisfies the rotational speed parameter, and stores time sampled sensor data received from the plurality of sensors relative to a time zero corresponding to a time when the rotational speed satisfies the rotational speed.

The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.