Using Pattern Recognition to Identify Design Errors on Pipe System Drawings

Verification activities are intended to reduce the costs of system development by identifying design errors before deploying the system. However, subcontractors in multifirm projects are motivated to implement locally cost‐effective verification strategies over verification strategies that benefit the main contractor. Incentivizing verification activities is one mechanism by which the contractor can motivate subcontractors to implement verification strategies desirable to the contractor. Prior work on mathematical models of verification in systems engineering has neither explored optimal verification strategies nor incentives in multi‐firm projects. In this paper, we present a modeling concept for determining optimal verification strategies in multi‐firm projects. Our models are belief‐based, which means that contractors and subcontractors incorporate their at times limited knowledge about true verification state through a probabilistic assessment of possible states. We develop an initial two‐level model, where one contractor directly works with multiple subcontractors at the next lower level. This model is then extended to a general network model with multiple, multilevel contractor‐subcontractor relationship. We derive solution algorithms that characterize the optimal verification strategies and incentives for each of the firms. Our work contributes to the systems engineering literature by laying the foundation for the study of incentives as a mechanism to align verification activities in multi‐firm systems engineering projects.

To identify design errors that escape pre-silicon verification, post-silicon debug is becoming an important step in the implementation flow of digital circuits. It is concerned with identifying design errors that escape to silicon. While commonly used in practice, it has received less research focus when compared to its complementary problem of manufacturing test, which is focused on screening for fabrication defects. We provide the background and summarize some recent research that addresses the emerging challenges.

PurposeThe purpose of this paper is to develop a computationally efficient and generally applicable measure for a pipe routing problem which decides the pipe paths and affects the pipe assembly feasibility. By imitating human thinking in pipe assembly planning, human's experience and intuition are quantified and applied in the pipe routing algorithm.Design/methodology/approachHuman's imaginal thinking is simulated with procedures of knowledge representation, pattern recognition, and logical deduction; the algorithm transforms the physical obstacles and constraints into 3D pipe routing space and then into 2D planar projection, by using convex hull algorithm onto the projection, the shortest pipe route is found efficiently.FindingsA novel pipe assembly planning algorithm by imitating human imaginal thinking is presented, which effectively solved the problem of conceiving the shortest pipe route in 3D space with obstacles and constraints.Practical implicationsThe application of the algorithm in assembly planning of an aircraft engine pipe system is demonstrated. The algorithm can also be used in similar pipe planning problems such as industrial plant pipe planning and submarine pipe system design.Originality/valueHuman's imaginal thinking is introduced into pipe routing algorithm for the first time. By using graphics as the media to transfer the pipe routing information, human's experience and intuition in pipe assembly planning are quantified and computationally applicable.

Intelligent fault diagnosis systems of equipment are essential in industries, to prevent failures, reduce downtime and increase plant availability. In this context, this work proposes a methodology to develop diagnostic systems for detecting anomalies and identifying failures in the piping system due to malfunctioning pumps, such as cavitation. Relevant information is extracted based on motor electric current, available in PROFINET networks by smart relays, widely used in this type of application. A prototype of a hydraulic piping system was created for data collection and methodology validation. Machine learning tools for extracting and selecting features (such as standard deviation, entropy, harmonics, among others), and an SVM-based model for pattern recognition, are used to diagnose the status of the centrifugal pump. The results achieved were 88.7 % of accuracy for cavitation fault detection and 100.0% for dry running operation. Thus, the methodology is feasible and efficient, indicating an opportunity to improve the use of data available by smart relays in industrial network communication.

The issue of microplastics in the aquatic environment has become one of the key topics in contemporary environmental engineering, chemical engineering and materials technology. Plastic microparticles are found not only in natural waters, but also in industrial and municipal piping systems, process installations and even in drinking water, posing a growing threat to public health, ecosystem stability and the reliability of technical equipment. Due to its chemical resistance, hydrophobicity and variety of sizes and shapes, microplastics are difficult to remove using traditional separation methods, and their harmful impact is part of a broader analysis of the life cycle of plastics, from their production and use to the waste phase and their impact on the environment. In response to the scale of the phenomenon, a number of liquid–solid separation methods have been developed, including approaches based on physical, chemical and biological principles. These methods vary in their scope of application, operational requirements and the way they interact with the particles present in the flow. The scientific literature describes mechanical techniques, chemical reactions and the interaction of biological organisms in a controlled environment as the main groups of separation. Each group has specific limitations resulting from the properties of microplastics, flow conditions and medium characteristics, which means that the choice of separation technology must take into account the specific nature of the system in question. The development of advanced measurement methods, monitoring systems and control techniques enables more accurate observation and analysis of particle movement, as well as the study of the relationship between device operating parameters and the behaviour of contaminants in the flow. The increasingly widespread use of measurement data, predictive algorithms and pattern recognition techniques makes it possible to describe the phenomena accompanying microplastic separation in greater detail and to formulate new concepts for devices and flow systems based on analytical methods, computational tools and adaptive control systems is in line with current trends in process engineering and automation, as well as with the concept of Industry 4.0. Taking the above information into account, the aim of this work is to analyse selected liquid–solid separation methods in order to identify the most optimal in terms of the effectiveness of removing plastic microparticles, with the assumption of the greatest possible number of features indicating the possible future automation of a given process.

The hardware in a loop concept is introduced for the design of wireless communication chipsets. The aim is to verify the algorithm design followed by extensive software simulations of the proposed algorithms. Signalion provides a rapid prototyping platform to enable this algorithm verification on a hardware. This approach helps to identify design errors and implementation faults. The hardware platform can be used for complex PHY implementations such as MIMO-OFDM modems.

Exploring the rate of adoption and implementation depth of building information modeling (BIM): A case of Kuwait

Moldy grain have mildew which are harmful to people and animals. To provide a simple and objective solution to identify whether grain are moldy, a smell analyzing system of grain quality is developed. The system consists of gas sensor array, gas pipe system, signal adjusting circuit, data collecting system and pattern recognition system. The software is developed on the basis of Visual Basic 6.0 and Matlab 6.5. In order to improve veracity of identification, three eigenvalues in the collecting data are used, one is the maximal response point, the other two are near the maximal response point. The sample eigenvalues are trained with the triplex-optimize BP NN. By testing, both training samples and test samples can be identified correctly, which indicates that the application of neural networks is valuable in the smell analyzing system of grain quality.