ENBIS Spring Meeting 2017

28 – 30 May 2017; Monastery of Schlägl in Upper Austria Abstract submission: 11 November 2016 – 5 March 2017

My abstracts


The following abstracts have been accepted for this event:

  • Preventive Maintenance of Wood-Framed Buildings for Hurricane Preparedness

    Authors: Spandan Mishra (Florida State University, Department of Industrial and Manufacturing Engineering), Arda Vanli (Florida State University, Department of Industrial and Manufacturing Engineering), Grzegorz Kakareko (Florida State University, Department of Industrial and Manufacturing Engineering), Sungmoon Jung (Florida State University, Department of Industrial and Manufacturing Engineering)
    Primary area of focus / application: Reliability
    Secondary area of focus / application: Quality
    Keywords: Degradation modelling, Preventive maintenance, Age-replacement, Hurricane loss
    Submitted at 20-Jan-2017 18:26 by Arda Vanli
    Accepted (view paper)
    30-May-2017 11:15 Preventive Maintenance of Wood-Framed Buildings for Hurricane Preparedness
    This paper presents a new time-dependent reliability modeling and optimal maintenance planning approach for residential building components subject to hurricane winds. A gamma process is employed to model the stochastic degradation of components. Time-dependent fragility and failure probabilities obtained from the degradation models are in turn used to determine the retrofit timing that optimize an average cost per cycle time. The method is illustrated on determining the retrofit times of roof-to-wall connections based on actual failure data. Effectiveness of the optimal retrofit age is compared to a replacement age based on time-invariant failure probabilities. It is shown how the effect of wind speed uncertainty on maintenance cost can be quantified using the proposed method.
  • Reliability Growth Monitoring in Product Validation

    Authors: Stefan Müllner (CIS Consulting in Industrial Statistics GmbH)
    Primary area of focus / application: Quality
    Secondary area of focus / application: Modelling
    Keywords: Reliability, Reliability growth, Product validation, Product testing, Reliability monitoring, Failure mode, Failure rate
    Submitted at 12-Feb-2017 13:15 by Stefan Müllner
    30-May-2017 12:35 Reliability Growth Monitoring in Product Validation
    In the development of complex products reliability is becoming a more and more important aspect. From the engineering perspective the aim is to increase the reliability of a complex system by testing it in various situations which cover the range of customer use appropriately. During the tests the failures occurring are recorded and monitored over the course of time. In a successful test programme, the number of failures over time decreases due to corrections implemented into the system, and thus reliability growth can be monitored. Developing tests in order to detect failure causes and modes and verify system reliability is an important objective in reliability engineering.

    We will present a monitoring algorithm which allows the user to assess reliability and make predictions at arbitrary time points. Furthermore the algorithm determines whether a previously specified reliability target will be reached, and proposes actions in case the reliability target is unlikely to be attained.

    The monitoring algorithm applies the Crow-AMSAA reliability growth tracking model. Estimations are made by using the Maximum Likelihood method, and confidence bounds for the model are computed by applying the Fisher matrix approach. The reliability growth monitoring algorithm may be applied to both exact failure time data and grouped data, in which case the failure counts in time intervals are known only. The algorithm was developed such that it can be used for data in which no information of failure modes is available, and incorporates the information resulting in more detailed evaluations otherwise.

    Application of the algorithm is shown in several examples with real-life datasets. Analyses and predictions are performed retrospectively and the goodness-of-fit of the Crow-AMSAA model is evaluated.
  • ICAR – An Integrated Method to Create and Assess Reliability Test Plans

    Authors: Christoph Ruf (Daimler AG), Nikolaus Haselgruber (CIS Consulting in Industrial Statistics GmbH)
    Primary area of focus / application: Reliability
    Keywords: Reliability, Test Planning, Design of Experiments, Practical application
    Submitted at 13-Feb-2017 10:00 by Christoph Ruf
    30-May-2017 11:35 ICAR – An Integrated Method to Create and Assess Reliability Test Plans
    Due to very tight time schedules in the development of heavy duty trucks there is limited time for planning reliability activities. Nevertheless, a sufficiently valid statement is expected.
    ICAR is a collection of methods for creation and assessing test programs. These test programs need to have a robust design (DoE) and the capability to demonstrate a reliability target. To get the best trade-off between testing budget and the predicted warranty costs it is also possible to adjust the test program by reducing these total costs till an optimum.
    The aim of ICAR is to obtain a statistically reliable, methodologically verifiable and reproducible statement with few assumptions. A practical application of the ICAR method will be shown.
  • Monitoring Offshore Wind Turbines

    Authors: Alessandro Di Bucchianico (Eindhoven University of Technology), Stella Kapodistria (Eindhoven University of Technology), Thomas Kenbeek (Eindhoven University of Technology)
    Primary area of focus / application: Process
    Secondary area of focus / application: Reliability
    Keywords: Condition based maintenance, Statistical Process Control, Offshore windturbine, Renewable energy
    Submitted at 15-Feb-2017 10:31 by Alessandro Di Bucchianico
    Accepted (view paper)
    29-May-2017 09:45 Monitoring Offshore Wind Turbines
    Undetected damage to parts of a wind turbine such as blade cracks due to lightning or broken gear wheels may have disastrous consequences possibly leading to loss of the entire wind turbine. It is therefore important to continuously monitor the condition of wind turbines, in particular when they are placed at remote locations (e.g., offshore wind farms). Technological advances make it economically feasible to equip wind turbines with sensors for various physical variables (including vibration).
    We describe our experiences when applying Statistical Process Control to monitor the condition of wind turbines in the Netherlands that are equipped with various sensors. Our approach is based on jointly monitoring variables using regression analysis to correct for external influences. This was an eye opener for the wind turbine engineers who use to think in threshold values for individual sensor variables. Analysis of historical data showed that malfunctioning of one the generators of a specific wind turbine could have detected several months before the actual breakdown of the complete gearbox. This research was performed within the DAISY4OFFSHORE (Dynamic Asset Information System for Offshore Wind Farm Optimisation) project funded by the Dutch government through its “Wind at Sea” Top Consortium Knowledge and Innovation. The work of Kapodistria is also supported by the Dutch Science Foundation Gravitation Project “Networks” (www.thenetworkcenter.nl).
  • Optimized Production Processes Using Sensor Data Analysis

    Authors: Simone Lombardi (MathWorks), Sarah Drewes (MathWorks)
    Primary area of focus / application: Process
    Keywords: Predictive maintenance, MATLAB, Machine learning, Data analytics
    Submitted at 17-Feb-2017 14:42 by Alessandro Tarchini
    29-May-2017 11:35 Optimized Production Processes Using Sensor Data Analysis
    Mondi Gronau cooperated with MathWorks Consulting to optimize production processes in the polymer film industry. The considered approach minimized waste production, downtimes and energy consumption while increasing production quality.

    In the main part of the project, a recommendation system was created that combines automated sensor data analysis and human experience. In this application, process information is constantly
    updated. Deviations trigger a warning message for the machine operator, such than he can intervene to reduce waste production.

    These recommendations are based on prediction models created with MATLAB using machine learning on historical sensor data. Per machine, hundreds of sensors (temperature, pressure, etc.) are monitored per minute.
    At the same time, quality states of the produced polymer film are also automatically captured.
    The sensor data is cleaned and consolidated with the state information, and different machine learning methods are evaluated on historical data. The most robust models with the best prediction accuracy is then used for the predictions.
    The system is integrated into the existing IT infrastructure and is used for an increasing number of production machines.
  • Predictive Maintenance of Turbofan Engines

    Authors: Simone Lombardi (MathWorks)
    Primary area of focus / application: Other: MATLAB Software Showcase
    Keywords: Data analytics, Machine learning, Predictive maintenance, MATLAB
    Submitted at 17-Feb-2017 15:22 by Alessandro Tarchini
    29-May-2017 13:45 Predictive Maintenance of Turbofan Engines
    Engineering and IT teams are using MATLAB to build today’s advanced Big Data Analytics systems ranging from predictive maintenance and telematics to advanced driver assistance systems and sensor analytics. Teams select MATLAB because it offers essential capabilities not easily accessible in business intelligence systems or open source languages.

    Through a series of examples, we will demonstrate several MATLAB capabilities in various domains:
    - Physical-world data: MATLAB has native support for sensor, image, video, telemetry, binary, and other real-time formats. Explore this data using MATLAB MapReduce functionality for Hadoop, and by connecting interfaces to ODBC/JDBC databases.
    - Machine learning, neural networks, statistics, and beyond: MATLAB offers a full set of statistics and machine learning functionality, plus advanced methods such as nonlinear optimization, system identification, and thousands of prebuilt algorithms for image and video processing, financial modeling, control system design.
    - High speed processing of large data sets. MATLAB’s numeric routines scale directly to parallel processing on clusters and cloud.
    - Online and real-time deployment: MATLAB integrates into enterprise systems, clusters, and clouds, and can be targeted to real-time embedded hardware.

    A case-study about how to use MATLAB to build a predictive maintenance system for turbofan engine for aviation applications will be shown. Particularly, this case-study will be focused on the development of a predictive maintenance system to predict when engine failures will occur on engines based on their live sensor data. The data used for the development of the predictive model are provided by NASA PCoE and acquired from 100 different engines of the same model. The predictive model developed can classify the engine status in urgency classes of maintenance, which are defined in accordance with the expected number of flights before failure.