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Monitoring & Control Intelligent Software Agents for Machinery Diagnostics Machinery plant maintenance represents a major expense for ship operators. In addition to normal main- tenance expenditures, unexpected breakdowns have a significant cost impact. Recognizing the importance of preventing equipment failures, companies continue to adopt automation for machinery monitoring at a rapid pace. Continual improvements in the price/perfor- mance ratio of automation systems have also fueled their increased use in shipboard machinery plant mon- itoring. As a result, more ships with automated machinery plants exist today than ever before. However, just as a typical Internet search can easily create "information overload", so too can process automation create a "data overload" situation for the engineering crews responsi- ble for equipment operation and maintenance. Modern shipboard automation systems typically monitor upwards of over 5,000 real-time process variables, allowing engineers to view massive amounts of data. This is one example of how automation systems can create too much data. Who is responsible for or has the time to monitor the 5,000 data points? How many peo- ple will this take and how much will it cost? The trends toward more automation, too much data, and too few people make it difficult for companies to assimilate data into information useful for optimal maintenance management. Machinery performance monitoring and maintenance management is an area where immediate exploitation of software agent tech- nology can yield substantial benefits. Software agents can serve as expert assistants in monitoring, control- ling, and troubleshooting complex machinery process- es. Agents can perform tedious, repetitive, and analyti- cally complex tasks without being constantly con- trolled by people. They can also provide valuable assis- tance in maintenance management decision-making. Meet Dexter MACSEA has offered its DEXTER machinery diag- nostic system since 1991. This system monitors alarm conditions, detects trends, diagnoses machinery faults, and predicts impending problems. DEXTER's artificial intelligence is based on neural network technology that diagnoses machinery faults and ranks them by their probability. The company has recently adapted its diag- nostic technology into a team of cooperating, real-time agents, allowing users to create as many agent assis- tants as needed for their particular condition monitor- ing requirements. The agents "plug-and-play" with most process control software and automation systems in use throughout industry. Tools are provided to build diagnostic knowledgebases that cause neural networks to be created automatically. The neural networks are then attached to different agents, which gives them the artificial intelligence to carry out their monitoring and diagnostic tasks. DEXTER's agents run continuously in the back- ground under Windows NT. Users can deploy multiple agents simultaneously, with each monitoring a differ- ent piece of equipment. Human-like animated charac- ters provide a simple user-agent interface employing the latest speech synthesis and recognition technolo- gies. Agent characters issue alerts only when they have diagnosed or predicted problems in the machinery MACSEA has offered its DEXTER machinery diagnostic sys tem since 1991. This system monitors alarm conditions, detects trends, diagnoses machinery faults, and predicts impending problems. I am detecting current Main Diesel Engine 1 faults. I can open the faults list. plant. They appear on a computer screen, no matter what other software you may be running at the time. At other times, the agent characters remain hidden, work- ing silently in the background. Since the agents are designed to run in a Windows NT environment, dis- tributed operation over local area networks is support- ed. With machinery plant maintenance often account- ing for up to 40 percent of total costs in a company, clearly knowledge assets in the maintenance area can be valuable. DEXTER is a tool that allows an organi- zation to capture, organize, manage, and distribute machinery diagnostic knowledge assets within your organization. Even the newest maintenance worker can immediately benefit from your diagnostic knowledge assets that are embedded into DEXTER's neural net- works. This knowledge can be distributed and exploit- ed across your entire maintenance operation, be it con- tained in a single factory or in several locations around the globe. The bottom line will be improved profitabil- ity through avoiding, reducing, or eliminating the con- sequences of machinery failures. Downtime due to equipment failure impacts both profitability and pro- ductivity by reducing output, increasing operating costs, and interfering with customer service. Mainte- nance plays a critical role in preserving the physical, financial, and competitive health of your company. Smart companies need to equip themselves now with smart tools for condition-based maintenance; smart tools like DEXTER. Cloning Human Intelligence A knowledgebase encapsulates valuable engineering knowledge about a machinery plant and its equipment. A knowledgebase is typically developed through an expert-level assessment of machinery failure modes. In maintenance circles, this is called a Failure Mode and Effects Analysis (FMEA) of the machinery plant. The FMEA involves enumerating all likely machinery faults based on information gathered from historical experience, manufacturers' troubleshooting informa- tion, and assessments of industry experts. Each fault is then characterized by its measurable symptoms in the plant, as monitored by the available sensor instrumen- tation and plant automation. A symptom is defined as an alarm condition, such as a particular temperature measuring HIGH, with respect to a set point limit. The FMEA forms the basis of diagnostic knowledge about the plant. A comprehensive FMEA of a machin- ery plant typically involves a substantial amount of time and effort. Because of this, any knowledgebase created from the FMEA becomes a valuable corporate intellectual asset, particularly when it is used with DEXTER as part of a condition-based, reliability-cen- tered maintenance program. A knowledgebase is a col- lection of information relating machinery faults and symptoms derived from the FMEA. The BRAINS tool allows a user to create and manage diagnostic knowledgebases that are used by DEX- TER's agents. DEXTER's diagnostic neural networks automatically learn the fault-symptom relationships you enter into each knowledgebase. Its software agents are then able to perform real-time diagnostics and prognostics of machinery plant problems. Besides entering a fault name and description, a user can also indicate any corrective actions or special instructions that the maintenance engineer should fol- low if the fault is detected. The information entered on this form will be displayed when this fault is detected (Continued on page 53) 47
