Analysis With Histogram of Connectivity: For Automated Evaluation of Piping Layout

An autonomous framework to evaluate layout of a piping design in the form of piping and instrumentation diagram (P&ID) according to a set of standards of marine and offshore industry is proposed. The method starts with transforming a P&ID into a vector ${x}$ in $R^{d}$ . Transformation is done based on a concept introduced for piping known as Histogram of Connectivity. The proposed descriptor captures two essential properties of P&ID: attributes of each component and connectivity among the components. Next, linear support vector machine (SVM) is used to learn a classifier from existing compliant and noncompliant designs. Subsequently, the linear classifier can be used to check if an unseen design complies with the standards. In addition, to enable follow up on noncompliant design including correction or modification, a method to analyze the reason of noncompliance prediction by the learned SVM model is introduced. The method has demonstrated encouraging performance in two challenging data sets of designs created with advice from experienced engineers in the industry, based on International Convention for the Prevention of Pollution from Ships (MARPOL) and Rules for Classification of Ships of Lloyd’s Register. Note to Practitioners—This paper is motivated by need of marine and offshore industry for automated solution for design appraisal. This paper aims to address this issue by using a machine learning-based approach. Some compliant and noncompliant designs are provided to a developed algorithm for a machine (or computer) to learn. After learning is completed, the machine is able to classify unseen designs as compliant or noncompliant. As highlighted in this paper, the developed method has demonstrated encouraging performance in two case studies, including specific parts in MARPOL and Rules of Lloyd’s Register. For adoption by industry, necessary steps include collecting some designs (compliant and noncompliant) available in an organization and feeding these into the developed method for learning by machine before it can predict. With ability of highlighting possible connections that cause noncompliance, follow up and correction on a noncompliant design is made possible.