scholarly journals A data-driven predictive maintenance strategy based on accurate failure prognostics

2021 ◽  
Vol 23 (2) ◽  
pp. 387-394
Author(s):  
Chuang Chen ◽  
Cunsong Wang ◽  
Ningyun Lu ◽  
Bin Jiang ◽  
Yin Xing
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Engineering Systems ◽  
Data Set ◽  
Periodic Maintenance ◽  
Maintenance Decisions ◽  
Aero Engines ◽  
Failure Prognostics

Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals Diffuse approximation for identification of the mechanical properties of microcapsules

2020 ◽  
pp. 108128652097760
Author(s):  
Carlos Quesada ◽  
Claire Dupont ◽  
Pierre Villon ◽  
Anne-Virginie Salsac
Keyword(s):  
Mechanical Properties ◽  
Active Material ◽  
Liquid Core ◽  
Constitutive Law ◽  
Data Driven ◽  
Membrane Properties ◽  
Mechanical Resistance ◽  
Data Set ◽  
Diffuse Approximation ◽  
Capsule Size

A novel data-driven real-time procedure based on diffuse approximation is proposed to characterize the mechanical behavior of liquid-core microcapsules from their deformed shape and identify the mechanical properties of the submicron-thick membrane that protects the inner core through inverse analysis. The method first involves experimentally acquiring the deformed shape that a given microcapsule takes at steady state when it flows through a microfluidic microchannel of comparable cross-sectional size. From the mid-plane capsule profile, we deduce two characteristic geometric quantities that uniquely characterize the shape taken by the microcapsule under external hydrodynamic stresses. To identify the values of the unknown rigidity of the membrane and of the size of the capsule, we compare the geometric quantities with the values predicted numerically using a fluid-structure-interaction model by solving the three-dimensional capsule-flow interactions. The complete numerical data set is obtained off-line by systematically varying the governing parameters of the problem, i.e. the capsule-to-tube confinement ratio, and the capillary number, which is the ratio of the viscous to elastic forces. We show that diffuse approximation efficiently estimates the unknown mechanical resistance of the capsule membrane. We validate the data-driven procedure by applying it to the geometric and mechanical characterization of ovalbumin microcapsules (diameter of the order of a few tens of microns). As soon as the capsule is sufficiently deformed to exhibit a parachute shape at the rear, the capsule size and surface shear modulus are determined with an accuracy of 0.2% and 2.7%, respectively, as compared with 2–3% and 25% without it, in the best cases (Hu et al. Characterizing the membrane properties of capsules flowing in a square-section microfluidic channel: Effects of the membrane constitutive law. Phys Rev E 2013; 87(6): 063008). Diffuse approximation thus allows the capsule size and membrane elastic resistance to be provided quasi-instantly with very high precision. This opens interesting perspectives for industrial applications that require tight control of the capsule mechanical properties in order to secure their behavior when they transport active material.

Using Risk Based Maintenance Optimization RBMO to Save Costs

2021 ◽  
Author(s):  
Khaled Ahmed Farouk Mohamed
Keyword(s):  
Risk Mitigation ◽  
Critical Item ◽  
Cost Benefit ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Critical Systems ◽  
Risk Matrix ◽  
Monetary Value ◽  
Maintenance Program ◽  
Systems Maintenance

Abstract Maintenance is a crucial pillar in plant integrity and availability. Saving money in maintenance should be established without affecting the asset's integrity. Based on this, the core of work is to maximize the maintenance return on investment (ROI). Maintenance ROI is the ratio between invested money in maintenance to mitigated risks due to maintenance actions. The objective is to minimize maintenance cost while maximizing assets integrity and availability. RBMO starts with ‘Maintenance Criticality Assessment’ (MCA) at unit/system level to define high (20 % of systems that represent 80% of risks), medium (20% of systems that represent 15% of risks), and low critical systems (60% of systems that represent only 5% of risks). Based on system criticality, a dedicated risk assessment is implemented to evaluate risks at tag level to define the worst maintenance action/s. High critical systems’ maintenance programs are developed using ‘Reliability-Centered Maintenance’ (RCM). Medium critical system maintenance program is developed using ‘Failure Mode, Effects and criticality analysis’ (FMECA). "Maintenance strategy for Low Critical item" guideline document is developed to define the best maintenance strategy for low critical units. All risks are evaluated using the standard ADNOC risk matrix. The risk is converted to monetary value in $ to evaluate maintenance actions using a formula. A special program was developed to facilitate MCA evaluation for each system and represent risk as monetary value using ADNOC Risk Matrix taking into consideration the redundancy and demand on a system during operation. MCAs were completed for all ADNOC Onshore Assets, see results below. Optimization starts by evaluating maintenance programs for low critical systems to save costs where low critical systems represent 50% to 60% of total systems in ADNOC Onshore. Based on this the total number of work orders has decreased by 6856, which is equivalent to saving $1M annually. In parallel, RCMs are conducted on high critical systems. Risk mitigation calculator in $ value was developed and embedded in the RCM information sheet to calculate cost benefit from implementing maintenance programs that were developed. RBMO is a systematic and traceable methodology to minimize maintenance cost and at the same time maximize system integrity and availability. This work showed the importance of reviewing the low critical systems’ maintenance program, as a first step in RBMO after implementing MCA, where low critical systems represent 50% to 60% of total assets and only 5% of total risks. ADNOC Onshore developed a dedicated guideline document "Maintenance Strategy for Low Critical Item" to facilitate decision making for proper maintenance strategy for low critical systems. Adding RCM risk mitigation calculator to RCM to calculate RCM cost benefit.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Yugang Yu ◽  
Gad Allon ◽  
Meiyan Wang ◽  
Zhentai Zhang
Keyword(s):  
Operations Management ◽  
Service Operations ◽  
Optimization Techniques ◽  
Data Driven ◽  
Analytical Models ◽  
Data Set ◽  
Service Operations Management ◽  
Level Data ◽  
Home Appliance ◽  
Research Challenge

To support the 2021 Manufacturing & Service Operations Management (MSOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers.

A New Uncertainty Analysis-Based Framework for Data-Driven Computational Mechanics

2021 ◽  
pp. 1-22
Author(s):  
Xu Guo ◽  
Zongliang Du ◽  
Chang Liu ◽  
Shan Tang
Keyword(s):  
Phase Space ◽  
Uncertainty Analysis ◽  
Distinctive Feature ◽  
Computational Mechanics ◽  
Problem Formulation ◽  
Solution Set ◽  
Data Driven ◽  
Data Set ◽  
Classical Counterpart ◽  
Single Solution

Abstract In the present paper, a new uncertainty analysis-based framework for data-driven computational mechanics (DDCM) is established. Compared with its practical classical counterpart, the distinctive feature of this framework is that uncertainty analysis is introduced into the corresponding problem formulation explicitly. Instated of only focusing on a single solution in phase space, a solution set is sought for in order to account for the influence of the multi-source uncertainties associated with the data set on the data-driven solutions. An illustrative example provided shows that the proposed framework is not only conceptually new, but also has the potential of circumventing the intrinsic numerical difficulties pertaining to the classical DDCM framework.

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