136 results about "Wavelet packet analysis" patented technology

The invention relates to a voltage transformer on-load tap-changer mechanical fault diagnosis method of which key technical points are that a vibration detection probe is adhered to a box wall of an on-load tap-changer in a surface-mounted manner, vibration signals generated in tap-changer operation processes can be captured, the vibration signals are subjected to energy frequency band decomposition operation via wavelet packet analysis technologies, frequency spectrum characteristics of the signals can be extracted, a state characteristic vector can be formed, a characteristic vector database is formed via signal collection and storage, an Euclidean distance function is used for analyzing a changing trend of the characteristic vector, and a mechanical state of the on-load tap-changer is diagnosed and assessed. Via the voltage transformer on-load tap-changer mechanical fault diagnosis method, the vibration signals generated in the tap-changer operation processes can be captured in real time, a characteristic quantity of the vibration signals is extracted and is compared with historical data, whether a fault occurs is determined and a fault development trend is determined, overhaul cost is lowered, detection efficiency is raised, early stage mechanical faults can be found timely and effectively, and the faults can be prevented from worsening.

The invention belongs to the technical field of human body differentiation and provides a method for extracting and differentiating human body heat source features of infrared pyroelectricity wavelet packet energy. According to the method, the purposes of detection within a 360-degree large-scale range and with a long distance and detection of a static infrared heat source are achieved and the problems that the comfort and the energy-saving performance in an intelligent air conditioner conflict and the false alarm rate in a safety supervision system is high are effectively solved. Thus, according to the technical scheme, the method for extracting and differentiating the human body heat source features of infrared pyroelectricity wavelet packet energy comprises the following steps that a single pyroelectricity infrared detector is driven by a stepping motor to rotate at a constant speed, so that detection of a static object at the long distance and within 360-degree large-scale range is achieved; the detection range is within a disc, wavelet packet analysis is conducted on a collected sample of the human body heat source and a collected sample of a non-human-body heat source, wavelet packet energy serves as a feature of a signal, 5-folding-time cross validation is conducted through a BP neural network, and therefore the human body heat source and the non-human-body heat source are differentiated. The method for extracting and differentiating the human body heat source features of infrared pyroelectricity wavelet packet energy is mainly applied to occasions of the human body differentiation technology.

The invention relates to a microgrid fault diagnosis method for optimizing an extreme learning machine based on a whale algorithm. The method comprises the steps of: S1, building a microgrid grid-connected operation simulation model comprising a wind driven generator, a photovoltaic cell and a storage battery, and collecting three-phase fault voltage signals at two ends of a line; S2, selecting adb6 wavelet as a wavelet basis, decomposing and reconstructing the three-phase fault voltage signals containing the phase A, the phase B and the phase C obtained by simulation according to a wavelet packet analysis related formula, calculating the energy entropies of the three-phase fault voltage signals to obtain a feature vector X= [x1, x2,..., x24] T containing 24 wavelet packet energy entropies, and taking the feature vector as a data sample; and S3, utilizing a whale algorithm WOA to optimize an input weight and a hidden layer threshold of an extreme learning machine ELM to establish a WOA-ELM fault diagnosis model, and substituting the data sample obtained in the S2 into the WOA-ELM model to carry out training and verification. A BP neural network, an RBF neural network and the ELM are utilized to establish the diagnosis model, the diagnosis model and the WOA-ELM model are subjected to comparative analysis, and the effectiveness and reliability of the WOA-ELM model are verified.

The invention discloses a method for monitoring a marine propulsion shafting bearing vibration failure. The method comprises the following steps: (1) creating a marine propulsion shafting vibration template library in an off-line mode; (2) synchronously collecting various monitor variables, including marine propulsion shafting bearing vibration acceleration signals, the host rotating speed, the propeller shaft rotating speed and the gearbox joining state; (3) classifying data; (4) performing time-frequency transformation on the classified vibration acceleration speed data by adopting a wavelet packet analysis method to acquire energy feature vectors on different feature frequency bands, transforming feature data of different frequency bands, and extracting the energy entropy feature vectors of the feature data to serve as to-be-matched feature vectors; (5) monitoring a failure, namely determining a specific failure type according to the similarity measure matching result; and (6) online updating the marine propulsion shafting vibration template library by utilizing the energy entropy feature vector which has no failure found in detection. The method is convenient and feasible to collect data, can be used for online monitoring and failure diagnosis, and can meet the requirement of long-term failure diagnosis and analysis.

The invention relates to a coal rock character identification method based on multiple parameters of cutting motors of a coal cutter. The coal rock character identification method is achieved by virtue of a coal rock character identification system based on the multiple parameters of the cutting motors of the coal cutter. The coal rock character identification system comprises a large-volume data storage device, an electrical main controller of the coal cutter, an intrinsically safe vibration acceleration sensor, a vibration data transmission cable, a communication data transmission wire and an onboard power supply of the coal cutter. The coal rock character identification method and the coal rock character identification system are achieved in the manners that working parameters consisting of vibration signals, the voltages, currents and temperature torques of left and right cutting motors and a traction motor of the coal cutter, the pressures of left and right rocker arm lifting oil cylinders of the coal cutter as well as speed signals of the traction motor are collected and stored, the signal features of coal and rock cut by cutting teeth of a roller of the coal cutter at different top and base plates are analyzed, the energy features of the vibration signals at different frequency bands are analyzed by virtue of a wavelet packet, the feature distribution of the working parameters of the cutting motors is analyzed by virtue of probability statistic samples, and different coal rock character databases are established by virtue of a multi-sensor information fusion technique.