A vacuum pump includes a control unit that monitors and controls a motor and a magnetic bearing, each being stored in a pump body. A temperature-adjustment function unit measures a temperature of the pump body by at least one temperature sensor disposed in the pump body and controls at least one heater or solenoid controlled valve based on the temperature. The temperature-adjustment function unit includes a first terminal capable of connecting or disconnecting the temperature sensor and a second terminal capable of connecting or disconnecting one of the heater and the solenoid controlled valve. A self-diagnosis unit capable of conducting a self-diagnosis of whether an input signal to the first terminal has been normally inputted or whether the signal has been normally outputted from the second terminal.

Disclosed are an overload protection apparatus and method, and a storage medium, a compressor and an electric appliance. The apparatus includes: a first overload protection mechanism and a second overload protection mechanism, wherein the first overload protection mechanism is arranged to perform overload protection on the pressure of a compressor to be protected, and/or the second overload protection mechanism is arranged to perform overload protection on at least one of the temperature and the current of the compressor to be protected.

The present invention provides wireless sensor technology seamlessly integrated into a pump system having a pump, a motor and a drive, has diagnostic and prognostic intelligence that utilizes sensor data, allows real-time condition monitoring; enables easy access to data and analytics via smart devices (i.e., smart phones and tablets); allows for easy remote monitoring (i.e., web portal) of the pump system; allows self-learning artificial intelligence (AI) built-in that adapts to changing conditions; and allows for smart pump system remote control. In operation, the present invention monitors the health and performance of the pump system that allows the user to get real-time data and intelligence virtually anywhere and anytime, as well as real-time diagnostics and prognostics, and also allows for smart control of the pump system remotely via smart device, and reduces downtime of equipment.

A pump includes a pump housing, an inlet, an outlet, a rotatable eccentric, a deformable element between housing and eccentric and a delivery channel from inlet to outlet formed by the deformable element and the housing. The deformable element is pressed against the housing in sections by the eccentric forming a movable seal of the channel and a closed volume in the channel being movable along the channel from inlet to outlet to pump the liquid by rotating the eccentric. A method for operating the pump includes a) setting a liquid quantity to be pumped, b) determining a temperature of the deformable element, c) determining a parameter considering the temperature from step b), the parameter representing a dependence between movement of the eccentric and pump capacity and d) pumping the liquid quantity set in step a) by adapting an operating mode of the pump considering the parameter from step c).

A computer implemented method of predicting a future maintenance event of a pumping equipment on a wellbore pumping unit comprising loading a pump usage log and a pump maintenance log into a predictive maintenance model. The predictive maintenance model is trained by a machine learning process with a historical database of completed pumping jobs. The predictive maintenance model determines a probability of a future maintenance event in response to the current pump usage. The unit controller displays an alert of the remaining pump life in comparison to a threshold value for a recommended pump maintenance period or a required pump maintenance period.

A computer implemented method of predicting a future maintenance event of a pumping equipment on a wellbore pumping unit comprising loading a pump usage log and a pump maintenance log into a predictive maintenance model. The predictive maintenance model is trained by a machine learning process with a historical database of completed pumping jobs. The predictive maintenance model determines a probability of a future maintenance event in response to the current pump usage. The unit controller displays an alert of the remaining pump life in comparison to a threshold value for a recommended pump maintenance period or a required pump maintenance period.

A compressor may include a shell, a compression mechanism, first and second temperature sensors, and a control module. The shell may define a lubricant sump. The compression mechanism may be disposed within the shell and may be operable to compress a working fluid. The first temperature sensor may be at least partially disposed within the shell at a first position. The second temperature sensor may be at least partially disposed within the shell at a second position that is vertically higher than the first position. The control module may be in communication with the first and second temperature sensors and the pressure sensor and may determine whether a liquid level in the lubricant sump is below a predetermined level based on data received from the first and second temperature sensors.

A pump includes a pump housing, an inlet, an outlet, a rotatable eccentric, a deformable element between housing and eccentric and a delivery channel from inlet to outlet formed by the deformable element and the housing. The deformable element is pressed against the housing in sections by the eccentric forming a movable seal of the channel and a closed volume in the channel being movable along the channel from inlet to outlet to pump the liquid by rotating the eccentric. A method for operating the pump includes a) setting a liquid quantity to be pumped, b) determining a temperature of the deformable element, c) determining a parameter considering the temperature from step b), the parameter representing a dependence between movement of the eccentric and pump capacity and d) pumping the liquid quantity set in step a) by adapting an operating mode of the pump considering the parameter from step c).

A computer implemented method of predicting a future maintenance event of a pumping equipment on a wellbore pumping unit comprising loading a pump usage log and a pump maintenance log into a predictive maintenance model. The predictive maintenance model is trained by a machine learning process with a historical database of completed pumping jobs. The predictive maintenance model determines a probability of a future maintenance event in response to the current pump usage. The unit controller displays an alert of the remaining pump life in comparison to a threshold value for a recommended pump maintenance period or a required pump maintenance period.