外文翻译-基于神经网络的逻辑故障监测的PLC控制系统.doc

1、英文原文PLC control logic error monitoring and prediction using Neural NetworkAbstractThis paper reviews monitoring and error prediction of PLC program using Neural Network. In the PLC device controlled manufacturing line, PLC program holds place of underlying component. It becomes controlling mechanism

2、. The level of automation in the production line relies on control mechanism practiced. In the modern manufacturing, PLC devices can handle whole production line given that structured and smart PLC program is executed. In other words, PLC program can manage whole process structure consisting set of

3、procedures. We present a method to monitor PLC program and PLC error prediction it using neural network. The neural network method being predictive in nature, it rigorously can monitor process signals from sensors, sensed during operation of PLC devices or execution of PLC program. Subsequently, a n

4、eural network algorithm practiced for the analysis of signals. In this way, thorough monitoring of PLC program can find possible errors from temporal parameters (e.g. Voltage, bias etc). In addition, possible alterations in program and irregularities can be minimized. That can result, easily to use

5、in fault detection, maintenance, and decision support in manufacturing organization. Similarly, it can lessen down-time of machines and prevent possible risks.Keywords: PLC, Artificial Neural Network (ANN), Fault-detection, Error prediction, Monitoring.1. IntroductionIn the modern manufacturing, the

6、 PLC is well-adopted to a range of automation tasks. These are typically industrial processes where changes to the system would be expected during its operational life and the production systems that feature cost of maintaining is relatively higher than cost of automation 1. PLC is special-purpose c

7、omputer, which is designed for multiple input and output arrangements, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. The reason behind increasing popularity of PLC (Programmable Logic Controller) is flexibility in control; the possible changes in

8、manufacturing controlling are performed through PLC program. The PLC program determines automation level of a manufacturing industry. In other words, the whole process structure of production line can be modeled and controlled by providing set of instructions to PLC. In this way, PLC program becomes

9、 underlying component of modern manufacturing. However, because of PLC nonflexible programming system relative to high level languages, their ability in fault detection and diagnosis is limited. The continuous monitoring of PLC program is vital to decrease machine down-times, safety-critical reasons

10、 and prevent potential risks. The diagnosis of PLC program becomes difficult because of data characteristic involved in process: analog and discrete 2. PLC devices execute programs scanning continuously and operate involved machines sending instructions as I/O in the discrete or digital format. Howe

11、ver, pressure, temperature, flow, and weights are often represented with integer values. Hence, input and output signals are represented in either binary or integer values; there are always chances of alterations in the values in the real time running production line. That is, the originally sound P

12、LC program may behave abnormally due to the changes in input and output values. Using neural network for fault diagnosis is not common as for vision or speech processing, however many successful applications have been reported notably 3. Anns are a form of artificial intelligence, which, by means of

13、 their architecture, attempt to simulate the biological structure of the human brain and nervous system. Although the concept of artificial neurons was first introduced in 1943, research into applications of Anns has blossomed since the introduction of the back-propagation training algorithm for fee

14、d-forward Anns in 1986 4. Anns may thus be considered a relatively new tool in the field of prediction and forecasting. When feed-forward Anns are used for prediction and forecasting, the modeling philosophy employed is similar to that used in the development of more conventional statistical models.

15、 In both cases, the purpose of the model is to capture the relationship between a historical set of model inputs and corresponding outputs. This is achieved by repeatedly presenting examples of the input/output relationship to the model and adjusting the model coefficients in an attempt to minimize

16、an error function between the historical outputs and the outputs predicted by the model. Although some ANN models are not significantly different from a number of standard statistical models, they are extremely valuable as they belong to the class of data driven approaches, whereas conventional statistical methods are model driven. In the former, the data are used to determine the structure of the model as well as the unknown model parameters. The use of ANN models m