35results about How to "Improve troubleshooting performance" patented technology

A data collection device is disclosed, which is used to collect data according to received instructions, including: N analog boards, the N analog boards are sequentially connected in series, and N is a natural number greater than or equal to 1 for collecting data; digital boards , connected to the first analog board in the N analog boards, for forwarding the received instructions to the N analog boards, and uploading the data collected by the N analog boards according to the instructions; wherein, The first analog board among the N analog boards receives the instructions forwarded by the digital board, and sequentially forwards them to the next-level analog boards, and the N analog boards collect data according to the received instructions, and forward them sequentially to the upper The primary analog board is sent to the digital board through the first analog board among the N analog boards. The invention also provides a data acquisition system and method thereof, which have strong anti-interference ability, strong real-time data transmission ability, high data transmission bandwidth, convenient fault diagnosis and high reliability.

The present invention provides a system and a method for the fault diagnosis of a blast furnace. The system comprises a historical data acquisition module, an actual data acquisition module, a feature weight matrix construction module, a model building module and a blast furnace fault diagnosis module. The method includes the steps of collecting the actual attribute data, the historical attribute data and the corresponding fault state types of the production condition of a blast furnace; according to the importance degree of each attribute for the fault diagnosis, determining the feature weight of the attribute and constructing a feature weight matrix; establishing a twin hyper-sphere support vector machine model for the feature weighting of the fault diagnosis of the blast furnace; taking the actual attribute data of the production condition of the blast furnace into the above established twin hyper-sphere support vector machine model to obtain the operation fault state type of the blast furnace that corresponds to the actual attribute data of the production condition of the blast furnace; and completing the fault diagnosis of the blast furnace. According to the technical scheme of the invention, the importance degree of each attribute for the fault diagnosis of the blast furnace is quantized. Meanwhile, the importance degree of each attribute is integrated into the constructing process of a learning machine. Therefore, the accuracy of the fault diagnosis is improved.

The present invention provides a real-time intelligent diagnosis method and system for bearing faults based on the Attention CNN model, including using a vibration sensor to collect faulty bearing vibration signals, and then segmenting the faulty bearing vibration signals using a fixed-length random segmentation method to obtain data samples; After the data samples are affixed with labels corresponding to each type according to the state type of the rolling bearing, they are divided into a training set, a verification set and a test set according to a certain ratio; according to the data in the training set and the verification set, a variety of data in the The bearing fault data set in the unbalanced state and all the bearing fault data sets produced constitute the unbalanced data set; construct the above model, train the above model with different bearing fault data sets respectively, and obtain the above training model; use the above training model Real-time fault detection is performed on the rolling bearing. The invention can identify the operating state of the bearing in real time, accurately and automatically, thereby effectively maintaining the normal operation of mechanical equipment.

The invention provides a transformer partial discharge fault diagnosis method based on hierarchical threshold synchrosqueezed wavelet. The method is characterized by comprising the following steps of: 1, subjecting a transformer partial discharge signal f(t) to SWT decomposition to obtain an SWT decomposition coefficient S<y><a(i)>(t), wherein the i = 1, 2, L, I; 2, estimating a noise standard deviation [sigma]n by using a formula described in the specification; 3, assigning values to ca(i) step by step at a fixed step length for the ith coefficient of the SWT, calculating the minimum mean square error by a mean square error formula, and determining the optimal value of the ca(i); 4, calculating the hierarchical threshold [gamma]a(i) = ca(i) [sigma]n, and achieving the ith SWT coefficient noise reduction through the ith SWT coefficient formula; 5, using a noise reduction formula to reconstruct de-noised layer coefficients to obtain a de-noised de-noising transformer local discharge signal y(t); and 6, performing feature extraction on the de-noised de-noising transformer local discharge signal y(t), and performing fault diagnosis according to extracted features.

The invention discloses a transformer fault type diagnosis method based on semi-supervised DBNC, which includes: selecting a sample data set; dividing the sample data into a pre-training set without labels, a labeled set, a test set 1 and a test set 2; State encoding of fault types; establishment of a transformer fault diagnosis model based on a deep belief network classifier; initialization of the parameters of each layer of the model; use of contrastive divergence to train each RBM at the bottom layer by layer; Optimize the parameters to make the network classification performance reach the global optimum; save the trained network, and use the sample data of the test set 1 to verify the classification performance of the network; solve the problem of using deep learning network fault data for transformer fault diagnosis Analysis and processing, usually only a small number of complete data samples can be obtained, and it is very difficult to obtain a large number of complete data samples with labels, which requires a lot of manpower and material resources.