1、- 1 - 附录 A 英文原文 A. Kusiak and A. Burns, Mining Temporal Data: A Coal-Fired Boiler Case Study, Proceedings of the 9thInternational Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, in R. Khosla, R.J. Howlett, L.C. Jain (Eds), Knowledge-Based Intelligent Information and Engineering Sy
2、stems: Vol. III, LNAI 3683, Springer, Heidelberg, Germany, 2005, pp. 953-958. Mining Temporal Data: A Coal-Fired Boiler CaseStudy Andrew Kusiak and Alex Burns Intelligent Systems Laboratory, Industrial Engineering 3131 SeamansCenter, The University of Iowa Iowa City, IA52242 1527, USA andrew-kusiaku
3、iowa.edu Abstract This paper presents an approach to control pluggage of a coal-fired boiler. The proposed approach involves statistics, data partitioning, parameter reduction, and data mining. The proposed approach was tested on a 750 MW commercial coal-fired boiler affected with a fouling problem
4、that leads to boiler pluggage that causes unscheduled shutdowns. The rare-event detection approach presented in the paper identified several critical time-based data segments that are indicative of the ash pluggage. 1 Introduction The ability to predict and avoid rare events in time series data is a
5、 - 2 - challenge that could be addressed by data mining approaches. Difficulties arise from the fact that often a significant volume of data describes normal conditions and only a small amount of data may be available for rare events. This problem is further exacerbated by the fact that traditional
6、data mining does not account for the time dependency of the temporal data. The approach presented in this paper overcomes these concerns by defining time windows. The approach presented in this paper is based on the two main concepts. The first is that the decision-tree data-mining algorithm capture
7、s the subtle parameter relationships that cause the rare event to occur 1. The second concept is that partitioning the data using time windows provides the ability to capture and describe sequences of events that may cause the rare failure. 2 Event Detection Procedure In the case study discussed in
8、the next section rare events can be detected by applying the five step procedure. These five steps include: Step 1: Parameter Categorization The parameter list is divided into two categories, response parameters and impact parameters. Response parameters are those that change values due to a rare ev
9、ent or a failure, e.g., an air leak in a pressurized chamber. Impact parameters are defined as parameters that are either directly or indirectly controllable and may cause the rare event. These are the parameters that are of greatest interest for the determination of rare events. Step 2: Time Segmen
10、tation Time segmentation deals with partitioning and labeling the data into time windows (TWs). A time widow is defined as a set of observations in chronological order that describe a specified amount of continuous observations. This step allows the data mining algorithms to account for the temporal
11、 nature of the data. The most effective method to segment the data is by - 3 - determining/estimating the approximate date of failure and set that as the last observation of the final time window. Step 3: Statistical and Visual Analysis This step involves statistical analysis of the data in each tim
12、e period that was designated in the previous step. Process shifts, changes in variation, and mean shifts in parameters are helpful in indicating that the appropriate time windows and parameters were selected. Step 4: Knowledge Extraction Data mining algorithms discover relationships among parameters
13、 and an outcome in the form of IF THEN rules and other constructs (e.g., decision tables) 1, 5. Data mining is natural extension of more traditional tools such as neural networks, multivariable algorithms, or traditional statistics. In the detection of rare events, the decision-tree and rule-inducti
14、on algorithms are explored for two significant reasons. First, the algorithms generate explicit knowledge in the form understandable by a user. The user is able to understand the extracted knowledge, assess its usefulness, and learn new and interesting concepts. Secondly, the data mining algorithms
15、have been shown to produce highly accurate knowledge in many domains. Step 5: Analysis of Knowledge and Validation This step deals with validation of the knowledge generated by the data mining algorithm. If a validation data set is available it should be used to validate the accuracy of the rules. I
16、f no similar data is available then unused data from the analysis or a 10-fold cross-validation can be utilized 6. 3 Power Boiler Case Study The approach proposed in this research was applied to power plant data. Data mining algorithms are well suited for electric power applications that produce hundreds of data points at any time instance. This case study deals with an ash fouling condition that causes boiler
