52results about How to "Realize online diagnosis" patented technology

The invention discloses a method for examining loosing of iron tower bolts. The method combines the iron tower vision measurement technology with theoretical calculation to predict loosing of bolts, and adopts the vision measurement method to measure vibration of iron towers. By adopting the non-contact method, potential safety hazard caused by iron tower climbing measurement is avoided, natural wind is utilized as the power source, and the method is time-saving and labor-saving as compared with artificial iron tower climbing cable-lanyard measurement.

The invention discloses a dynamic diagnosis method of a throttle valve. The method includes the steps that the target opening value of the throttle valve is acquired; the static deviation range valueof the throttle valve is acquired according to the target opening value; the actual dynamic deviation limiting value of the throttle valve is acquired; the position deviation range value of the throttle valve is acquired according to the actual dynamic deviation limiting value and the static deviation range value; the opening error of the throttle valve is acquired; whether the opening error of the throttle valve is within the position deviation range value or not is judged; if not, it is determined that the throttle valve breaks down. By means of the dynamic diagnosis method of the throttle valve, the throttle valve is dynamically diagnosed under the condition without dependent on a direct current motor and other equipment, the working state of the throttle valve is detected in real time,and timely feedback generated when the fault occurs is achieved.

The invention discloses a diagnosis method, a cloud server and a system of a universal energy station equipment, wherein the method includes: obtaining the relevant state parameters of each process equipment according to the type of each process equipment of a plurality of process equipment; judging each process equipment Whether the relevant status parameters of the equipment meet the preset conditions; if the preset conditions are met, the current operating status of each process equipment is obtained according to the relevant status parameters of each process equipment; according to the current operating status of each process equipment and related status parameters Predict the running trend within the preset time, and get the diagnosis result according to the running trend within the preset time. This method can obtain the diagnosis result according to the running trend within the preset time, so that various performance indicators of the equipment can be monitored in real time, and the purpose of online diagnosis of the pan-energy station equipment based on the industrial cloud can be realized, the accuracy of the diagnosis can be improved, and the accuracy of the diagnosis can be improved. The real-time performance can effectively ensure the reliability of monitoring and reduce operating costs.

The invention relates to an on-line diagnosis and evaluation method of the insulation state of a large electric machine, aiming at accurately diagnosing and evaluating the insulation state by comprehensively considering the influences of working environmental factors, such as vibration, temperature, humidity, and the like, of the electric machine on partial discharge. The on-line diagnosis and evaluation method comprises the following steps of: firstly building a neural network used for identifying discharge modes; constructing a neural network frame in an MATLAB2007 (Matrix Laboratory 2007); extracting a sample from a sample library to train the neural network; respectively building relational models of various discharge modes and the working environmental factors influencing the discharge modes according to the action relationship of the working environmental factors on the different discharge modes; installing various sensors on the large electric machines, and acquiring data; establishing a database; calculating a characteristic value; correcting to obtain a Qm value at a standard working environment; longitudinally comparing corrected Qm historical data read from the database with the current corrected Qm value, and horizontally comparing Qm among three phases of same equipment and among all electric machines so as to obtain the insulation state according to corresponding rules. The invention provides a diagnosis and evaluation conclusion of an insulation fault.

The embodiment of the invention provides an online positioning method for distribution network single-phase fault, which relates to the field of power system fault positioning and can judge a fault circuit without depending on zero-sequence current and realize quick online positioning on the distribution network single-phase grounding fault. The method of the specific scheme comprises steps: when SCADA (Supervisory Control And Data Acquisition) determines a single-phase fault judgment condition is met, a phase current recording command is sent to K RTUs (Remote Terminal Unit), wherein the phase current recording command comprises the fault happening time determined by the SCADA and the fault phase information; the K RTUs sample fault phase current signals in M cycles before and after the fault happening time, effective energy is obtained through wavelet transformation, and the effective energy is transmitted to the SCADA; and according to the effective energy returned by the K RTUs, the SCADA judges a single-phase fault line from lines monitored by the K RTUs. The method of the invention is used for judging the single-phase fault line.

The invention relates to an online diagnosis method for inverter open-circuit faults in a PMSM drive system, which can perform online fault diagnosis and location for various inverter open-circuit faults. Introduce the Lönberg observer to track the PMSM three-phase current, so as to obtain the residual error of the three-phase current observation as the basis for fault diagnosis; design a sampling strategy to obtain the original sample set for training; in the offline training, first introduce the principal component analysis method The original sample set is preprocessed to obtain a data dimensionality reduction model, and then the classification model is obtained by training the support vector machine; finally, the dimensionality reduction model and classification model are run in the drive system to realize online diagnosis and location of faults. The invention does not increase hardware equipment, does not affect drive control, and is not affected by load and model parameter changes, and through machine learning of actual working conditions, does not rely on manually set diagnosis basis, reducing the possibility of misdiagnosis.