The invention discloses a quality state task network based manufacturing system health diagnosis and prediction method. The quality state task network based manufacturing system health diagnosis and prediction method comprises the steps of 1, collecting running data of an analysis device; 2, building a quality state task network model of the manufacturing system; 3, quantifying a product quality state; 4, quantifying an equipment performance state; 5, quantifying a task execution state; 6, building a manufacturing system health diagnosis and prediction model and integrating the product quality state, the equipment performance state and the task execution state; and 7, analyzing and discussing the result. Through the method abovementioned, the manufacturing system health diagnosis and prediction model in the view of system engineering is built, comprehensive consideration for running polymorphism and constitution multi-station property of the manufacturing system is realized, the engineering purpose of improving the transparency of the running state of the system is improved, and the one-sidedness problems such that the traditional health diagnosis method ignores the production task and the manufactured product state are solved.

Quality state task network based manufacturing system health diagnosis and prediction method

The invention discloses an interval type index forecasting method based on a Bayesian network and an extreme learning machine, belongs to the fields of automatic control, information technologies and advanced manufacturing, and particularly relates to learning of parameters of an asymmetric Gaussian distribution Bayesian ELM model and adaptive adjustment on asymmetric weights. The interval type index forecasting method is characterized by including the following steps: as for the characteristic of the uncertainty of a complex production process, production indexes are described through the number of intervals, asymmetric Gaussian distribution serves as output distribution in an ELM model, the Bayesian ELM model with the weights is obtained, and the parameters of the Bayesian ELM model are learnt under an experience Bayesian frame through practical running data in the complex production process; on the basis, the pair of reciprocal weights are learnt with an adaptive adjustment method, and finally the forecasting value of the interval type indexes is obtained. By means of the interval type index forecasting method, the production indexes in the practical production process can be forecast, and the interval type index forecasting method can be used for guiding operation optimization and dynamic scheduling in the production process.

Interval type indicator forecasting method based on bayesian network and extreme learning machine

The invention relates to an automatic feeding method and system for multiple varieties and small batch of PCBs. The method comprises the following steps of: quantifying factor variable data and establishing a data model by analyzing and selecting a key influencing factor based on production feeding, scrap data and product order parameter information big data; embedding the data model into an ORACLE system and obtaining a system automatic prediction model; running a model program, automatically matching calculating the historical scrap rate of the orders in the generated system automatic prediction model, and automatically obtaining order information, the number of remainders and the number of online orders; predicting the scrap rate based on the order information and historical scrap rate; based on the predicted scrap rate, predicting the number of pre-fed materials of the order; checking whether abnormal data exists in the predicted scrap rate and the number of pre-fed materials of the order, and releasing production if not. The method and system eliminate difference in manual feeding experience, improve feeding stability, improve production efficiency, improve accuracy and promote automatic lean production.

Automatic feeding method and system for multiple varieties and small batch of PCBs

The invention discloses a transformer substation oil-immersed transformer state evaluation and correction method and system based on big data analysis. The method comprises the steps that firstly, building a basic state evaluation parameter system from multiple aspects based on 220kV transformer substation actual condition multi-source information; performing high-dimensional matrix dimension reduction processing on the intrinsic state criterion matrix, and establishing negative feedback correction by closed-loop dimension-increasing iterative correction; meanwhile, a new weight coefficient determination and correction method is provided for a given key parameter system, and a membership function is fitted to perform evidence body fusion so as to complete fuzzy evaluation. Finally, the utility function value is provided. The key parameters are expressed in the three-dimensional space of the big data level, and the computer platform architecture design and main program steps of the 220kV transformer state evaluation system are provided based on the C/S structure, so that a basis is provided for state maintenance through a more accurate evaluation result.

A transformer substation oil-immersed transformer state evaluation and correction method and system based on big data analysis

Embodiments of the invention provide a ship monitoring system, method and device. The ship monitoring system includes a shore-based monitoring subsystem and a sea-based monitoring subsystem, and the sea-based monitoring subsystem includes a distributed controller system, a sea-based data server, and a sea-based IMCS implemented based on a multi-layer artificial neural network. The sea-based IMCS is connected to the distributed controller system and the sea-based data server, and the sea-based data server is connected with the shore-based monitoring subsystem, wherein the distributed controllersystem is used for collecting operation data of a to-be-monitored system on a ship and sending the data to the sea-based IMCS for data learning, diagnosis and fault processing, and the sea-based dataserver is used for storing data and transmitting the sea-based monitoring data to the shore-based monitoring subsystem for processing. The system, method and device can realize real-time monitoring and condition processing of the navigation process of the ship, and effectively ensure the safety during the operation of the ship.

Ship monitoring system, method and device

The invention discloses a dynamic bottleneck analytical method of a semiconductor production line. The method includes the steps that firstly, a key parameter capable of representing the bottleneck device characteristic is selected to be used as a bottleneck analysis index; secondly, the comprehensive bottleneck degree of equipment is described quantificationally through a combined definition method from the aspects of the relative production load, the utilization rate, the queue length of a buffer area and the like of the equipment, the bottleneck equipment is recognized by combining a bottleneck judging mechanism, and an equipment number is obtained; furthermore, a bottleneck prediction model of the semiconductor production line is established through an increased trimming type neural network based on the key parameter and the bottleneck equipment number, and the network structure is corrected in a dynamic mode based on a closed-loop control concept; at last, the key parameter affecting the bottleneck is analyzed through a single-factor testing method, and the reasonable value range of the parameter is obtained during bottleneck stabilization. By the adoption of the method, the bottleneck equipment can be recognized rapidly and effectively, the bottleneck at the next moment can be predicted, bottleneck drift is analyzed quantificationally under the condition of the dynamic effect of the key parameter, and a basis is laid for optimal dispatching of the semiconductor production line.

Dynamic bottleneck analytical method of semiconductor production line

The invention relates to a semiconductor fabrication process multi-performance prediction method, comprising the steps of selecting an articles being processed level parameter, an equipment parameter and a workpiece parameter which represent the state of a semiconductor production line as influence factors of performance indexes; collecting relevant data of the production line, preprocessing by using a principal component analysis method, removing redundant information, constructing a multi-performance prediction model by using a Bayes neural network, and controlling the complexity of the network model by introducing a Bayes method; analyzing whether the model performance conforms to a performance evaluation criteria by a model precision proof method, and performing online correction on the network model structures which do not conform to the standard; finally determining the key factors influencing the average workpiece processing period and the equipment utilization rate. According to the semiconductor fabrication process multi-performance prediction method, the defects that the performance prediction model in the semiconductor field is limited by constraint conditions, the generalization performance is very poor and the like are improved, the problem that the single performance prediction model in the semiconductor field is not applicable to multi-performance prediction modeling is solved, and the method is an improvement of the semiconductor fabrication process multi-performance prediction method.

Semiconductor fabrication process multi-performance prediction method

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Dynamic bottleneck analytical method of semiconductor production line

Semiconductor fabrication process multi-performance prediction method