SYSTEM FOR MAINTAINING AND ANALYZING MANUFACTURING EQUIPMENT AND METHOD THEREOF
First Claim
1. A system for maintaining and analyzing a manufacturing equipment, adapted to manage and maintain a forecast model of the manufacturing equipment, the system comprising:
- an embedded forecast device, directly configured in the manufacturing equipment, wherein the embedded forecast device comprises a feature extraction (FE) algorithm and a forecast model therein for obtaining a real time data of the manufacturing equipment and generating a forecast result, and the embedded forecast device further comprises a novelty detection model for generating a model retraining notification message when the manufacturing equipment fails;
a server, comprising;
a communication interface, for communicating with the embedded forecast device;
a plurality of different FE algorithms and modeling algorithms;
an operating interface, for selecting and combining any one selected from the FE algorithms and the modeling algorithms, so as to train and build the forecast model and the novelty detection model; and
a parameter setting table, for recording setting parameters related to the training and building of the forecast model and the novelty detection model;
wherein the server the forecast model and the novelty detection model according to the model retraining notification message; and
updates the forecast model and the novelty detection model of the embedded forecast device according to the retrained forecast model and novelty detection model.
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Abstract
A system and a method are used for maintaining and analyzing manufacturing equipment. The system includes an embedded forecast device (EFD) configured in the manufacturing equipment and a server in communication with the EFD. The EFD is built in with a feature extraction (FE) algorithm and a forecast model (FM), so as to obtain a real time data of the manufacturing equipment and carry out forecasts on the manufacturing equipment to generate a forecast result. The server has various types of FE algorithms and modeling algorithms, which are selected and combined by a user for training and building a required FM and setting related parameters. The embedded forecast device also has a novelty detection model (NDM), which is capable of informing the server to retrain the models when the manufacturing equipment fails and then to update the FM and the NDM of the embedded forecast device.
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Citations
14 Claims
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1. A system for maintaining and analyzing a manufacturing equipment, adapted to manage and maintain a forecast model of the manufacturing equipment, the system comprising:
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an embedded forecast device, directly configured in the manufacturing equipment, wherein the embedded forecast device comprises a feature extraction (FE) algorithm and a forecast model therein for obtaining a real time data of the manufacturing equipment and generating a forecast result, and the embedded forecast device further comprises a novelty detection model for generating a model retraining notification message when the manufacturing equipment fails; a server, comprising; a communication interface, for communicating with the embedded forecast device; a plurality of different FE algorithms and modeling algorithms; an operating interface, for selecting and combining any one selected from the FE algorithms and the modeling algorithms, so as to train and build the forecast model and the novelty detection model; and a parameter setting table, for recording setting parameters related to the training and building of the forecast model and the novelty detection model; wherein the server the forecast model and the novelty detection model according to the model retraining notification message; and
updates the forecast model and the novelty detection model of the embedded forecast device according to the retrained forecast model and novelty detection model. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for maintaining and analyzing a manufacturing equipment, adapted to manage and maintain models of a plurality of manufacturing equipments and automatically update the models, the method comprising:
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providing an embedded forecast device, wherein the embedded forecast device is pre-loaded with a feature extraction (FE) algorithm, a novelty detection model, and a forecast model, the embedded forecast device is mounted in the manufacturing equipment for obtaining a real time data of the manufacturing equipment, and then the FE algorithm is used to extract a feature data from the obtained real time data, so as to generate a forecast result through the forecast model; building a server, wherein the server is pre-loaded with various FE algorithms and modeling algorithms, and an operating interface is provided for a user to select and combine any one selected from the FE algorithms and the modeling algorithms for training and building a required forecast model and novelty detection model and setting related parameters to be stored in a parameter table; performing a novelty detection on the real time data of the manufacturing equipment, and sending a model retraining notification message when a new feature data is found; retraining the models, wherein once receiving the model retraining notification message, the server retrains the forecast model and the novelty detection model according to the new feature data and support vectors of an old model; and updating the forecast model and the novelty detection model, wherein the server uploads the retrained forecast model and novelty detection model to the embedded forecast device of the manufacturing equipment for updating. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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Specification