Patent ID: 12253848

DETAILED DESCRIPTION OF THE INVENTION

Please refer toFIG.1, which is a diagram illustrating a machine abnormality marking and abnormality prediction system according to the present invention. As shown inFIG.1, the abnormality marking and abnormality prediction system1of the present invention machine is connected with a factory host5, and comprises a parameter streaming unit2, an abnormality reporting unit3and a prediction analysis unit4, wherein the parameter streaming unit2comprises: a streaming server21, a protocol server22, a database server23, and a static server24, wherein the streaming server21is connected with each of the machines61-63through the gateway device211, and comprises a shutdown module and maintenance module. The protocol server22provides an object linking and embedding (OLE) for process control (OPC), to read the shutdown and maintenance data of each machine in operation, and perform transmission according to the shutdown module and the maintenance module of the streaming server. The database server23is connected with the protocol server22, and comprises a redis cache to periodically store the shutdown and maintenance data of each of the machines61-63in operation from the protocol server22for cache use. The static server24is connected with the database server23, to count and average the data of each of the machines61-63in operation with a specific period (e.g., 1 minute), to accelerate the speed of reading subsequent data in various sizes.

The abnormality reporting unit3comprises at least one handheld device31, which can be a smart phone, tablet, or laptop. In this embodiment, the handheld device31is illustrated as a smart phone, which is held and used by a machine operator. The handheld device31is wirelessly connected with the streaming server21of the parameter streaming unit2, which is capable of transmitting, through the operation of the operator, the abnormality reasons and occurrence time caused by the shutdown of each machine in operation to the shutdown module for storage through the operation of the operator. In addition, the handheld device31can also transmit, through the operation of the operator, the abnormality reasons and occurrence time caused by the maintenance of each machine in operation to the shutdown module for recording.

The prediction analysis unit4comprises a microprocessor41, a time sequence recorder42and a neural network classifier43, which can be referred toFIG.2. The microprocessor41is connected with the static server24and the abnormality reporting unit3, and can store the data of each machine in operation calculated with the specific period (such as 1 minute) and averaged by the static server24as a historical data value. In addition, the microprocessor41can store the abnormality reasons and occurrence time caused by the shutdown and maintenance of each machine in operation reported by the abnormality reporting unit3as an instant data value. The instant data value can be also viewed as a referential prediction value for training the neural network classifier43. The time sequence recorder42is connected with the microprocessor41, and uses time sequence410with specific second, minute and hour, which is to take second as unit to define the time sequence of the past 60 seconds of said each machine in operation as Xsec, take minute as unit to define the time sequence of the past 30 minutes of said each machine in operation as Xmin, and take 30 minutes as unit to define the time sequence of the past 12 hours of said each machine in operation as Xhr, so as to extract one-dimensional vectors and connect them in series. The neural network classifier43is connected with and the time sequence recorder42, and comprises first trigger body431and second trigger body432. The first trigger body431utilizes the minimal difference to predict “the instant data value” (denoted by ypred) and the cross-entropy loss in “the historical data value” (denoted by y) according to “the instant data value and historical data value” received from the time sequence recorder every day, in order to optimize neural network module. That is, if “the instant data value” minus “the historical data value” is greater than the prediction error (denoted by ε1, where ε1=y−ypred), the neural network module will be updated by the markings of said specific period (i.e., the recent 30 days). On the other hand, if “the instant data value” minus “the historical data value” is smaller than the prediction error, the second trigger body432is utilized to receive “the instant data value and historical data value” from the time sequence recorder in a random sampling manner, and the equal amount “instant data value and historical data value” are used to test the precision of the neural network module constructed by the first trigger body431, that is, if the value of the previous prediction error (ε1) divided by the “historical data value” is greater than the later prediction error (denoted by ε2, where ε2=ε1/y), the random sampling of said specific period and another set of equal amount “instant data value and historical data value” will be used in prediction. That is, said each machine in operation is re-sampled randomly so as to build a new neural network module in cooperation with the equal amount “instant data value and historical data value” in past 30 days, so as to retrain, predict the states of machines, and provide abnormality warnings, so that the personnel in the factory can arrange production line maintenance or capacity adjustment in advance or adjust the machines of the factory production line, to avoid occasional shutdown and reduce factory losses.

Please refer toFIG.2, which is a diagram illustrating a prediction analysis unit of the abnormality marking and abnormality prediction system according to the present invention. As can be seen from the architecture of the prediction analysis unit4, the neural network classifier43takes the abnormality reason and occurrence time of each machine in operation shutdown and maintenance from the time sequence recorder42as the input, which adopts the sequence410with specific second, minute and hour, so as to use the first trigger body431and second trigger body432to perform continuous learning and training in neural network, and then output usable prediction values of the neural network module. In this way, the states of the machines can be predicted, and the abnormality markings and abnormality warnings can be thereby generated, so that the personnel in the factory can arrange production line maintenance or capacity adjustment in advance or adjust the machine of the factory production line, to avoid occasional shutdown and reduce factory losses.

To sum up, the virtual foreman dispatch planning system of the present invention can ensure the innovative purpose and meet the requirements of patent applications. However, what are described above are merely preferred embodiments of the present invention. Modifications and changes made according to the present invention shall fall into the scope of this patent application.