Image-based truss recognition for density-based topology optimization approach

Structural and Multidisciplinary Optimization - Topology optimization is a tool that supports the creativity of structural-designers and is used in various industries, from automotive to...     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.     

Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.     

A proposal for a classification of product-related dependencies in development of mechatronic products

When designing mechatronic products, ‘complex dependencies’ are often reported to be a major challenge. This paper focuses on managing dependencies between attributes of the product during the design process. The literature study shows that there is a gap in the literature with regard to the classification of product-related dependencies. Traditionally, these dependencies have been described as appearing between the following product attributes: function, properties and structure. By analysing three mechatronic projects from industry, we identified and classified 13 types of product-related dependencies. Each product-related dependency is described and illustrated using the practical examples from the industrial projects. The value of the classification is evaluated by applying it to an industrial development setting not used for the analysis. The evaluation shows that delays in the project schedule, loss of functionality and quality issues can be avoided if attention is directed toward the product-related dependencies in the development process.     

Fuzzy decision support for tools selection in the core front end activities of new product development

The innovation process may be divided into three main parts: the front end (FE), the new product development (NPD) process, and the commercialization. Every NPD process has a FE in which products and projects are defined. However, companies tend to begin the stages of FE without a clear definition or analysis of the process to go from Opportunity Identification to Concept Generation; as a result, the FE process is often aborted or forced to be restarted. Koen’s Model for the FE is composed of five phases. In each of the phases, several tools can be used by designers/managers in order to improve, structure, and organize their work. However, these tools tend to be selected and used in a heuristic manner. Additionally, some tools are more effective during certain phases of the FE than others. Using tools in the FE has a cost to the company, in terms of time, space needed, people involved, etc. Hence, an economic evaluation of the cost of tool usage is critical, and there is furthermore a need to characterize them in terms of their influence on the FE. This paper focuses on decision support for managers/designers in their process of assessing the cost of choosing/using tools in the core front end (CFE) activities identified by Koen, namely Opportunity Identification and Opportunity Analysis. This is achieved by first analyzing the influencing factors (firm context, industry context, macro-environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships. The decision support focuses upon the estimated investment needed for the use of tools during the CFE. The generation of FDSMs is carried out automatically using a specialized genetic algorithm, applied to learning data obtained from five experienced managers, working for five different companies. The automatically constructed FDSMs accurately reproduced the managers’ estimations using the learning data sets and were very robust when validated with hidden data sets. The developed models can be easily used for quick financial assessments of tools by the person responsible for the early stage of product development within a design team. The type of assessment proposed in this paper would better suit product development teams in companies that are cost-focused and where the trade-offs between what (material), who (staff), and how long (time) to involve in CFE activities can vary a lot and hence largely influence their financial performances later on in the NPD process.     

Urinary calculi in a geriatric setting

The authors report a retrospective series of 174 patients with urolithiasis. They were all age between 65 and 88 years. This group represents one tenth of all patients treated for urinary stones in the Urology department of Sfax over the last decade. Neither the clinical symptoms nor radiological findings observed in this group differed from those other patients hospitalized for urolithiasis; on the other hand associated diseases related to aging appear to be well represented which alters the management, prognosis and outcome in this type of patients.     

Clinical hemodialysis experience with percutaneous arteriovenous fistulas created using the Ellipsys® vascular access system

Introduction: The aim of this study is to report our clinical hemodialysis experience using a percutaneous arteriovenous fistula (pAVF) created with the Ellipsys® vascular access system. This pAVF device creates a permanent AVF anastomosis between the proximal radial artery (PRA) and the deep communicating vein (DCV) in the proximal forearm. Methods: The medical records of all patients with a pAVF were retrospectively reviewed. The clinical data analyzed included reliability of pAVF use, quality of dialysis, rate and success of puncture, and pAVF related complications, along with incidence of subsequent interventions. Findings: Between May 2017 and November 2018, 34 patients had a pAVF created with technical success in 33 patients (97%). Twenty‐eight out of 34 (82%) patients had successful two‐needle cannulation within 10 days to 6 weeks after pAVF creation. The mean Kt/v was 1.6 (1.2‐2) and the average recirculation was 10%. Fifteen patients (44%) needed no further access intervention. Twelve patients (35%) required an additional procedure to assist maturation of the pAVF in order to facilitate puncture. The average blood flow measured at the brachial artery, before the first cannulation, was 850 ml/min. From causes unrelated to the procedure, four patients died during the follow‐up study. Two patients required revision to a surgical AVF. None of the pAVFs developed aneurysmal degeneration steal syndrome, or high access flow related issues. Discussion: The Ellipsys® pAVF offers a safe and functional vascular access for hemodialysis. Advantages included prompt access maturation, avoidance of high flow AVFs, and a simple nonsurgical procedure with high patient satisfaction. Functional outcomes are equivalent and likely better than surgical fistulas. There appears to be less aneurysmal degeneration and need for future re‐intervention. Objective dialysis parameters indicate excellent quality of hemodialysis for the patient.     

Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling

Prediction of cutting forces is very important for the design of cutting tools and for process planning. This paper presents a fuzzy modelling method of cutting forces based on subtractive clustering. The subtractive clustering combined with the least-square algorithm identifies the fuzzy prediction model directly from the information obtained from the sensors. In the micro-milling experimental case study, four sets of cutting force data are used to generate the learning systems. The systems are tested against each other to choose the best model. The obtained results prove that the proposed solution has the capability to model the cutting force in spite of uncertainties in the micromilling process.