The data storage capacities of modern process automation systems have grown rapidly. Nowadays, the systems are able to frequently carry out even hundreds of measurements in parallel and store them in databases. However, these data are still rarely used in the analysis of processes. In this article, preparation of the raw data for further analysis is considered using feature extraction from signals by piecewise linear modeling. Prior to modeling, a preprocessing phase that removes some artifacts from the data is suggested. Because optimal models are computationally infeasible, fast heuristic algorithms must be utilized. Outlines for the optimal and some fast heuristic algorithms with modifications required by the preprocessing are given. In order to illustrate utilization of the features, a process diagnostics framework is presented. Among a large number of signals, the procedure finds the ones that best explain the observed short-term fluctuations in one signal. In the experiments, the piecewise linear modeling algorithms are compared using a massive data set from an operational paper machine. The use of piecewise linear representations in the analysis of changes in one real process measurement signal is demonstrated.

In the construction industry, projects are becoming increasingly large and complex, necessitating multiple subcontractors. Traditional centralized coordination techniques used by general contractors become insufficient as subcontractors perform most work and provide their own resources. When subcontractors cannot provide enough resources, they hinder their own performance, that of other subcontractors, and ultimately the entire project. Thus, projects need a new distributed coordination approach wherein all of the concerned subcontractors can respond to changes and reschedule a project dynamically. This paper presents a new distributed coordination framework for project schedule changes (DCPSC) that is based on an agent-based negotiation approach wherein software agents evaluate the impact of changes, simulate decisions, and give advice on behalf of the human subcontractors. A case example demonstrates the significance of the DCPSC. It compares two centralized coordination methodologies used in current practice to the DCPSC framework. We demonstrate that our DCPSC framework always finds a solution that is better than or equal to any of two centralized coordination methodologies.

Construction cost estimators are confronted with the challenging task of estimating the cost of constructing one of a kind facilities. They must first recognize the design conditions of the facility design that are important (i.e., incur a cost) and then determine how the design conditions affect the cost of construction. Current product models of facility designs explicitly represent components, attributes of components, and relationships between components. These designer-focused product models do not represent many of the cost-driving features of building product models, such as penetrations and component similarity. Previous research efforts identify many of the different features that affect construction costs, but they do not provide a formal and general way for practitioners to represent the features they care about according to their preferences. This paper presents the formal ontology we developed to represent construction knowledge about the cost-driving features of building product models. The ontology formalizes three classes of features, defines the attributes and functions of each feature type, and represents the relationships between the features explicitly. The descriptive semantics of the model allow estimators to represent their varied preferences for naming features, specifying features that result from component intersections and the similarity of components, and grouping features that affect a specific construction domain. A software prototype that implements the ontology enables estimators to transform designer-focused product models into estimator-focused, feature-based product models. Our tests show that estimators are able to generate and maintain cost estimates more accurately, consistently, and expeditiously with feature-based product models than with industry standard product models.

This paper presents an approach to the automatic generation of electromechanical engineering designs. We apply messy genetic algorithm (GA) optimization techniques to the evolution of assemblies composed of LegoTM structures. Each design is represented as a labeled assembly graph and is evaluated based on a set of behavior and structural equations. The initial populations are generated at random, and design candidates for subsequent generations are produced by user-specified selection techniques. Crossovers are applied by using cut and splice operators at the random points of the chromosomes; random mutations are applied to modify the graph with a certain low probability. This cycle continues until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies (Legos), as well as the creation of selection criteria for this domain. Our eventual goal is to introduce a simulation of electromechanical devices into our evaluation functions. We believe that this research creates a foundation for future work and it will apply GA techniques to the evolution of more complex and realistic electromechanical structures.