Intelligent Systems For more information on partnering with the Kansas City Plant, contact: Office of Business Development 1.800.225.8829 customer_inquiry@kcp.com
Machine Intelligence Machine intelligence is the integration of automated data analysis techniques with process knowledge. Its primary goal is to aid human operators in the analysis of data and in the interpretation of the resulting information. In this way, the analyst s role is changed from one of tedious data processing and information collection to one of data interpretation. This is the most effective way to leverage the knowledge and expertise of the particular user. The Kansas City Plant uses a variety of commercial and custom-designed machine intelligence tools to build application-specific solutions for customers. These tools include statistical pattern recognition methods, neural networks, fuzzy logic reasoning engines, genetic algorithms and expert systems. Our specific machine intelligence capabilities and development tools include: Anomaly and change detection, which uses neural networks and statistical pattern detection methods to characterize normal or consistent patterns so that deviations from those normal patterns can be detected Pattern classification, which uses neural networks and genetic algorithms for pattern detection and classification Clustering, predictive modeling and dependency modeling, which attempt to define a model for the process that generated the data Data mining, which uses feature extraction methods, data fusion algorithms and visualization techniques to identify patterns, correlations or anomalies within a collection of data or information Process automation and intelligent advisors, which use expert systems, fuzzy logic and knowledge capture methods to automate analysis processes and guide human analysts in the analysis and interpretation of highly complex, multi-source data Expert system for anomaly detection in nuclear material databases 45
Applications Database Anomaly Detection This application used knowledge acquisition and rule-based recognition techniques to develop an expert system that detects anomalies in nuclear material transaction databases at the Los Alamos National Laboratory. This expert system was designed to use six key features to characterize each nuclear material transaction (movement or process involving nuclear material). It allowed the database managers to define specialized search and query rules to sort through thousands of transactions on a weekly basis and to search for unusual or inappropriate activities within the database that may be indicative of the diversion of nuclear material. Machine Intelligence integrated data acquisition and real-time analysis to track transport vehicle positions along a route and send an alarm if anomalous behavior was identified. Multiple neural networks were trained to monitor the movement of the transport over a sequence of neighborhoods or partial paths and to compare past (normal) patterns of movement with the current movement, identifying any variances. This system provided: Autonomous tracking of the movement of transports from site to site (intra-facility) Real-time monitoring using a commercial off-the-shelf integrated global positioning system and radio frequency communication system Customized user interface to provide real-time operational support and to report on the nature and location of abnormal activity Nuclear Facility Monitoring Neural networks were used to process data acquired from a multi-sensor monitoring system to identify and characterize movement and activity inside a nuclear material processing and storage facility. The neural network was trained using data from three different sensors in the vault. The neural network fused these data and learned to associate specific patterns and correlations in the data streams with unique and specific movements and activities within the vault. Using relatively simple features from the multiple sensors, all major movement within the vault could be identified and characterized as normal or abnormal activity. Shipment tracking anomaly detection system Shipment Tracking and Anomaly Detection This system was designed to provide 46
Knowledge Preservation The Kansas City Plant s knowledge preservation program is a systematic approach for selecting, capturing, storing and distributing institutional knowledge. This process-mapdriven program uses video clips, audio clips, animations, text documents and graphics to explain critical manufacturing processes. Our system preserves not only information recorded in travelers, but also tacit knowledge that is often lost with workforce turnover. This valuable tool prevents future generations from making costly mistakes by teaching them the history and evolution of a process. It also results in reduced training time and improved learning curves for associates who are new to a given process. The knowledge preservation process consists of these major steps: Identifying and Prioritizing the Process Operational managers are briefed on the knowledge preservation process and selection criteria and then work with their line managers to create a list of candidates. includes work instructions and reports. Capturing Multimedia Information The knowledge preservation team interviews the SME, who is the key person in the capture process. The interviewer gathers any implicit knowledge the SME possesses that is unique and important to the process but not part of any formal documentation. A process map Recording an audio clip to explain a process Mapping the Process Once a process has been selected, process maps are developed by the subject matter expert (SME) in conjunction with the knowledge preservation team of experts. The knowledge preservation team uses failure mode effects analysis to identify the critical processes and steps to be captured and documented. Video documentation Gathering Process Documentation Standard procedures are documented and background information is collected. This 47
Developing and Delivering the Knowledge Preservation Program Once the interview and production have been filmed, the team edits the video footage into three distinct types of presentations to be preserved. These are process overviews, stepby-step descriptions and SME notes of the production process. The result is a sophisticated and easily navigable program that is always accessible from an online desktop computer. It is a program that allows the engineer, scientist or operator to run through the entire production process in a linear fashion or to navigate randomly by selecting a specific step or overview of several steps, SME commentary, process map or document for review. Knowledge Preservation Applications The Kansas City Plant has used the knowledge preservation tool to capture some of our most vital processes. Examples of processes that we have recorded include: Electrical Coded switch products MSAD detector hook cable assembly Round wire detonators Printed wiring boards Mechanical Contact block manufacturing process Burst disk assembly Rolamite assembly Fiber optic polishing of MT connectors Engineered Materials Commercial reservoir forging Solid silicone molding compound Physical vapor deposition Carbon syntactic foam The knowledge preservation browser window, with SME interview on the left and process map on the right 48
The Kansas City Plant has a long history of developing machine vision solutions for a wide array of applications. Machine vision techniques integrate data and image capture methodologies, automated image analysis and process knowledge to aid human operators in the analysis and interpretation of large collections of imagery. The Kansas City Plant has been nationally recognized for successful deployment of integrated machine vision applications with R&D 100, Federal Laboratory Consortium and Department of Energy 100 awards. Kansas City Plant associates also hold multiple patents and copyrights for machine vision solutions. Machine Vision - Performs visual inspection or characterization tasks in a highly repeatable and consistent manner - Reduces operator fatigue and operator error - Provides analysis and interpretation support to human analyst Process knowledge capture - Captures complex inspection and evaluation methodology used by human analysts - Improves the quality and throughput of the process - Can be used as an aid to train new personnel in the process The Kansas City Plant can design, develop and deploy machine vision applications to meet specific customer requirements. Advantages of using machine vision tools for application development include reduced analysis time, increased process throughput, knowledge capture, increased process repeatability and higher production quality. Specific tasks that may be performed through the use of machine vision tools include: Automated processing of data before presentation to human analysts - Performs tedious and repetitive aspects of the analysis process, reserving the interpretation of the processed results for the analyst - Increases throughput and reduces cost Image data mining and anomaly detection - Automatically processes large databases of imagery to identify common image features - Identifies unusual or anomalous image content Automated inspection and quality assurance Automated analysis of concrete microstructure 49
Applications Automated Detection of Agricultural Pathogens The Kansas City Plant developed and deployed an automated image processing and analysis system designed to automatically scan microscope slides, acquire optical imagery and automatically process the imagery to detect the presence of Karnal bunt spores (a fungal spore of wheat). This application reduced the amount of operator interaction time from 40 to 60 minutes to less than 15 minutes, greatly increasing the throughput and efficiency of their scanning and review process. The Kansas City Plant received a 1997 Federal Laboratory Consortium Award and a Department of Energy Energy 100 Award for this application. Automated Analysis of Diamond The Kansas City Plant developed an automated scanning and image analysis system to acquire imagery of a diamond from multiple views. The images were analyzed and used to locate the facets and vertices of the diamond so that a caret weight could be calculated. In addition, any defects within the diamond were identified from the imagery and placed on a 3D map of the stone, indicating the location and classification of the defect. The primary value of this system is consistency of the evaluation process and the ability to map all key structures within the diamond. Automated Concrete Microstructure Image Analysis The Kansas City Plant designed a machine vision system to automatically scan the surface of a concrete sample, acquire imagery Machine Vision and then automatically process the imagery to detect and classify all the components of the concrete. This automated system was built to replace the current manual process of concrete evaluation that required the human expert to spend between eight and 12 hours at a microscope to evaluate a sample. The automated system reduced the amount of operator interaction time to about 30 minutes. This product received a 2001 R&D 100 award and currently has a patent pending. Diamond feature analysis 50
The Kansas City Plant has significant experience in the development and deployment of pattern recognition systems for a wide range of applications. These methods are used to analyze and interpret both signal and image data to identify the presence of patterns and correlate these patterns across multiple, diverse data sets. Applications include the detection and identification of unknown patterns in a dataset (data mining), the search for and classification of known or repeatable patterns (classification and taxonomy), and the detection of anomalous or unexpected patterns (anomaly detection). Pattern recognition capabilities at the Kansas City Plant include: Digital signal and image processing techniques to process signal and image data and to extract salient features for development of pattern recognition tools Pattern detection and data mining techniques to analyze very large databases of information for patterns and to characterize and classify those patterns (signal taxonomy) Automated analysis methodologies to automatically process very large collections of signal and imagery data for upstream processing and to support analysis and interpretation by the human analyst Data and sensor fusion techniques for the integration and correlation of multiple data sets to improve pattern recognition capabilities Rapid prototyping and system integration of multi-sensor remote sensing platforms Pattern Recognition These capabilities offer numerous advantages: Increased productivity of analysts through the automation of tedious or repetitive tasks Increased process throughput Enhanced ability to integrate or fuse disparate data sets Reduced complexity of the information required for review by the analyst Improved process efficiency by freeing human operator to perform more complex tasks Preserved process knowledge Vehicle for training new personnel Applications The Kansas City Plant has deployed numerous applications for a diverse customer set, including the NNSA, the Department of Defense, the Department of Agriculture, the Department of Transportation and U.S. industry. Custom pattern recognition software 51
Pattern Recognition Data Mining and Information Fusion Data mining and information fusion (DM/IF) techniques are used to correlate data sets from multiple sensors or data acquisition systems to extract useful information about a physical system or process. The goal of these techniques is to extract more information about the system or process than could be gleaned by processing the individual data streams. Typically, DM/IF techniques are required to analyze and formulate solutions to highly complex problems that require the correlation of tens, or even hundreds, of data inputs. Such DM/IF solutions incorporate a variety of algorithms including statistical pattern recognition algorithms, artificial neural networks, rule-based systems or fuzzy logic. Feature Extraction and Evaluation Methods Feature extraction and evaluation are two procedures common to the development of any pattern recognition application. are the primary pieces of information extracted from raw data that are used to train the pattern recognition tool, whether that tool is a neural network, a fuzzy logic rule base or a genetic algorithm. Feature evaluation methods aid in the selection of the features to be used by the pattern recognition tool and can significantly streamline the development time of the pattern recognition application by identifying those features that are most significant to the final, developed solution. Automated Signal and Image Analysis The Kansas City Plant has developed and deployed a number of signal analysis, signal taxonomy and anomaly detection applications in support of remote sensing programs at the national laboratories: Development of algorithms to detect unique patterns in very large databases (thousands of signals) of satellite-acquired radio frequency, gamma ray and optical data The development of sensor fusion methods to correlate data from multiple sensors Design and deployment of image recognition algorithms for analysis of video scenes Remote Sensing, Subsurface Detection and Analysis The Kansas City Plant has developed several multi-sensor remote sensing systems for subsurface anomaly detection and site survey. Customized computer system architectures were developed to integrate all acquisition and analysis processes for real-time analysis of field data. This technology was used to support several different remote sensing applications to include anti-tank landmine detection for the U.S. Army, subsurface site survey and evaluation for the Department of Energy, and real-time forensics search and recovery to support law enforcement agencies in the collection of buried forensic evidence. 52