Data-driven approach to identify obsolete functions of products for design improvements

2021 ◽  
pp. 1-14
Author(s):  
Zhihua Zhao ◽  
Yupeng Li ◽  
Xuening Chu
Keyword(s):  
Time Series ◽  
Functional Performance ◽  
Smart Phone ◽  
Data Driven ◽  
Design Elements ◽  
Customer Expectations ◽  
Data Driven Approach ◽  
Identification Approach ◽  
Previous Identification

Identifying defective design elements is a prerequisite for design improvements. Previous identification methods were implemented in the context of static customer requirements (CRs). However, CRs always evolve continuously, which easily leads to a failure of existing product functions in fulfilling customer expectations; this, in turn, can lead to a decline in customer satisfaction. In this study, the phenomenon is termed as ‘function obsolescence’, and a data-driven identification approach for obsolete functions is proposed for design improvements. Firstly, product operating data are employed to construct the observing parameters of functional performance (OPs), and based on the distribution of OPs, the desired level of functional performance (DL) is defined to quantitatively characterise CRs. Secondly, the time series of DL is constructed to embody the evolution of CRs, in which a Sigmoid-like function is employed to establish a dissatisfaction function. With the time series, an obsolescence index measuring the severity of obsolescence for each function is defined to identify obsolete functions. A case study was implemented on a smart phone to identify its obsolete functions to demonstrate the effectiveness of the proposed methodology. The results show that some potentially obsolete functions can be identified by the proposed method considering the evolution of CRs.

scholarly journals Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach

2018 ◽  
Vol 28 (7) ◽  
pp. 075502 ◽  
Author(s):  
Francisco Traversaro ◽  
Francisco O. Redelico ◽  
Marcelo R. Risk ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach

A data-driven approach for denoising GNSS position time series

2017 ◽  
Vol 92 (8) ◽  
pp. 905-922 ◽  
Author(s):  
Yanyan Li ◽  
Caijun Xu ◽  
Lei Yi ◽  
Rongxin Fang
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach ◽  
Position Time Series ◽  
Gnss Position Time Series

scholarly journals Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

2020 ◽  
pp. 221-251
Author(s):  
Stefan Varga ◽  
Joel Brynielsson ◽  
Andreas Horndahl ◽  
Magnus Rosell
Keyword(s):  
Text Analysis ◽  
Ecological Validity ◽  
Data Driven ◽  
Intelligence Analysis ◽  
Prototype Tool ◽  
Analysis Task ◽  
Private Citizens ◽  
Data Driven Approach ◽  
Psychological Operations

Abstract With the availability of an abundance of data through the Internet, the premises to solve some intelligence analysis tasks have changed for the better. The study presented herein sets out to examine whether and how a data-driven approach can contribute to solve intelligence tasks. During a full day observational study, an ordinary military intelligence unit was divided into two uniform teams. Each team was independently asked to solve the same realistic intelligence analysis task. Both teams were allowed to use their ordinary set of tools, but in addition one team was also given access to a novel text analysis prototype tool specifically designed to support data-driven intelligence analysis of social media data. The results, obtained from the case study with a high ecological validity, suggest that the prototype tool provided valuable insights by bringing forth information from a more diverse set of sources, specifically from private citizens that would not have been easily discovered otherwise. Also, regardless of its objective contribution, the capabilities and the usage of the tool were embraced and subjectively perceived as useful by all involved analysts.

scholarly journals Conjunction of ANN and threshold based wavelet de-noising approach for forecasting suspended sediment load

2013 ◽  
Vol 3 (1) ◽  
pp. 09-25
Author(s):  
Vahid Nourani ◽  
Aida Yahyavi Rahimi ◽  
Farzad Hassan Nejad
Keyword(s):  
Time Series ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Data Driven ◽  
Suspended Sediment Load ◽  
Potomac River ◽  
Denoising Method ◽  
Raw Data ◽  
Daily Streamflow

Information on suspended sediment load (SSL) is fundamental for numerous water resources management and environmental protection projects. This phenomenon has the inherent complexity due to a large number of vague parameters and existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity turns into a barrier to get accurate prediction by conventional linear methods. On the other hand, the extent of the noise on hydrological data reduces the performance of data-driven models like Artificial Neural Networks (ANNs). Although ANNs could capture the complex nonlinear relationship between input and output parameters, being data-driven method positioned it in a state of need to preprocessed data. In this paper, the application of ANN approach focusing on wavelet- based denoising method for modeling daily streamflow-sediment relationship was proposed. The daily streamflow and SSL data observed at outlet of the Potomac River in USA were used as the case study. Achieving this purpose, Daubechies (db) was used as mother wavelet to decompose both streamflow and sediment time series into detailed and approximation subseries. Decomposition at level ten via db3 and at level eight via db5 were examined for streamflow and SSL time series, respectively. At first, the appropriate input combination with raw data to estimate current SSL was determined and re-imposed to ANN with denoised data.  The comparison of results reveals that in term of determination coefficient, the obtained result by denoised data was improved up to 23.2% with raged to use noisy, raw data and this exhibits that denoised data can be employed productively in ANN-based daily SSL forecasting.

scholarly journals A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6400
Author(s):  
Sara Antomarioni ◽  
Marjorie Maria Bellinello ◽  
Maurizio Bevilacqua ◽  
Filippo Emanuele Ciarapica ◽  
Renan Favarão da Silva ◽  
...  
Keyword(s):  
Power Plant ◽  
Failure Analysis ◽  
Power Plants ◽  
Failure Modes ◽  
Real Life ◽  
Hydroelectric Power Plant ◽  
Data Driven ◽  
Hydroelectric Power ◽  
Data Driven Approach

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.

scholarly journals A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1849 ◽  
Author(s):  
Mahmood Mahmoodian ◽  
Jairo Arturo Torres-Matallana ◽  
Ulrich Leopold ◽  
Georges Schutz ◽  
Francois H. L. R. Clemens
Keyword(s):  
Time Series ◽  
Storage Tank ◽  
Data Driven ◽  
Urban Drainage ◽  
Rainfall Time Series ◽  
Short Term ◽  
Time Step ◽  
Speed Up ◽  
Error Indicators

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

scholarly journals A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 827 ◽  
Author(s):  
Serafín Alonso ◽  
Antonio Morán ◽  
Miguel Prada ◽  
Perfecto Reguera ◽  
Juan Fuertes ◽  
...  
Keyword(s):  
Energy Demand ◽  
Data Driven ◽  
Coefficient Of Performance ◽  
Key Factors ◽  
Whole Plant ◽  
Data Driven Approach ◽  
Energy Efficiency Indicator ◽  
Cooling Loads ◽  
Aggregation Operations

Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.

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