This issue of AIEDAM focuses on AI in equipment service. Recently there has been a strong and renewed emphasis on AI technologies that can be used to monitor products and processes, detect incipient failures, identify possible faults (in various stages of development), determine preventive or corrective action, and generate a cost-efficient repair plan and monitor its execution. This renewed emphasis stems from a focus of manufacturing companies on the service market where they hope to grow their market share by offering their customers novel and aggressive service contracts. This service market includes power generation equipment, aircraft engines, medical imaging systems, and locomotives, just to name a few. In some of these new service offerings, the old parts-and-labor billing model is replaced by guaranteed uptime. This in turn places the motivation to maintain equipment in working order on the servicing company. Monitoring can be more efficiently accomplished, in part, by employing remotely monitored systems. Big strides have been taken for in-use monitoring of stationary equipment, such as manufacturing plants or high-end appliances, and also mobile systems such as transportation systems (vehicles, aircraft, locomotives, etc.). While advances in hardware development make it possible to perform these tasks efficiently, there are new avenues for progress in accompanying AI software techniques. Some of these approaches have their roots in efforts of years past while others arise from new challenges. Characteristics of typical challenges for AI in monitoring and diagnosis (M&D) service can be categorized into input, model, and output. In particular, input questions try

We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.

Product design and diagnosis are, today, worlds apart. Despite strong areas of overlap at the ontological level, traditional design process theory and practice does not recognize diagnosis as a part of the modeling process chain; neither do diagnosis knowledge engineering processes reference design modeling tasks as a source of knowledge acquisition. This paper presents the DAEDALUS knowledge engineering framework as a methodology for integrating design and diagnosis tasks, models, and modeling environments around a common Domain Ontology and Product Models Library. The approach organizes domain knowledge around the execution of a set of tasks in an enterprise product engineering task workflow. Each task employs a Task Application which uses a customized subset of the Domain Ontology—the Task Ontology—to construct a graphical Product Model. The Ontology is used to populate the models with relevant concepts (variables) and relations (relationships), thus serving as a concept dictionary-style mechanism for knowledge sharing and reuse across the different Task Applications. For inferencing, each task employs a local Problem-solving Method (PSM), and a Model-PSM Mapping, which operate on the local Product Model to produce reasoning outcomes. The use of a common Domain Ontology across tasks and models facilitates semantic consistency of variables and relations in constructing Bayesian networks for design and diagnosis.

The approach is motivated by inefficiencies encountered in cleanly exchanging and integrating design FMEA and diagnosis models. Demonstration software under development is intended to illustrate how the DAEDALUS framework can be applied to knowledge sharing and exchange between Bayesian network-based design FMEA and diagnosis modeling tasks. Anticipated limitations of the DAEDALUS methodology are discussed, as is its relationship to Tomiyama's Knowledge Intensive Engineering Framework (KIEF). DAEDALUS is grounded in formal knowledge engineering principles and methodologies established during the past decade. Finally, the framework is presented as one possible approach for improved integration of generalized design and diagnostic modeling and knowledge exchange.

For every problem mentioned by crew members in an aircraft log book, an associated repair action note is entered in the same log book by a maintenance technician after the problem has been handled. These hand-written repair notes, subsequently transcribed into a database, give an account of the actions undertaken by the technicians to fix the problems. Written in a free-text format with peculiar linguistic characteristics, including many arbitrary abbreviations and missing auxiliaries, they contain valuable information that can be used for decision support methods such as case-based reasoning. We use natural language techniques in our information extraction system to analyze the structure and contents of these notes in order to determine the pieces of equipment involved in a repair and what was done to them. Lexical information and domain knowledge are extracted from an electronic version of the illustrated parts catalog for the particular airplane, and are used at different stages of the process, from the morpholexical analysis to the evaluation of the semantic expression generated by the syntactical analyzer.

In equipment monitoring and diagnostics, it is very important to distinguish between a sensor failure and a system failure. In this paper, we develop a comprehensive methodology based on a hybrid system of AI and statistical techniques. The methodology is designed for monitoring complex equipment systems, which validates the sensor data, associates a degree of validity with each measurement, isolates faulty sensors, estimates the actual values despite faulty measurements, and detects incipient sensor failures. The methodology consists of four steps: redundancy creation, state prediction, sensor measurement validation and fusion, and fault detection through residue change detection. Through these four steps we use the information that can be obtained by looking at: information from a sensor individually, information from the sensor as part of a group of sensors, and the immediate history of the process that is being monitored. The advantage of this methodology is that it can detect multiple sensor failures, both abrupt as well as incipient. It can also detect subtle sensor failures such as drift in calibration and degradation of the sensor. The four-step methodology is applied to data from a gas turbine power plant.

The paper describes the task of performing efficient decision-theoretic troubleshooting of electromechanical devices. In general, this task is NP-complete, but under fairly strict assumptions, a greedy approach will yield an optimal sequence of actions, as discussed in the paper. This set of assumptions is weaker than the set proposed by Heckerman et al. (1995). However, the printing system domain, which motivated the research and which is described in detail in the paper, does not meet the requirements for the greedy approach, and a heuristic method is used. The method takes value of identification of the fault into account and it also performs a partial two-step look-ahead analysis. We compare the results of the heuristic method with optimal sequences of actions, and find only minor differences between the two.

This paper introduces an architecture for aggregation of output from different diagnostic tools. The diagnostic fusion tool deals with conflict resolution, where diagnostic tools disagree; temporal information discord, where the estimate of different tools is separated in time; differences in information updates, where the classifiers are updated at different rates; fault coverage discrepancies; and integration of a priori performance specifications. To this end, a hierarchical weight manipulation approach is introduced which creates and successively refines a fused output. The performance of the fusion tool is evaluated throughout its design. This allows impact assessment of adding heuristics and enables early tuning of parameters. Results obtained from diagnosing on-board faults from aircraft engines are shown which demonstrate the fusion tool's operation.

Modern industry is concerned about extending the lifetime of its critical processes and maintaining them only when required. Significant aspects of these trends include the ability to diagnose impending failures, prognosticate the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to condition-based maintenance (CBM). This paper attempts to address this challenging problem with intelligence-oriented techniques, specifically dynamic wavelet neural networks (DWNNs). DWNNs incorporate temporal information and storage capacity into their functionality so that they can predict into the future, carrying out fault prognostic tasks. Such fundamental issues as the network structure, learning algorithms, stability analysis, uncertainty management, and performance assessment are studied in a theoretical framework. An example is presented in which a trained DWNN successfully prognoses a defective bearing with a crack in its inner race.