Data-Driven Estimation of Heavy-Truck Residual Value at the Buy-Back

In a context of deep transformation of the entire automotive industry, starting from pervasive and native connectivity, commercial vehicles (heavy, light, and buses) are generating and transmitting much more data than passenger cars, with a much higher expected value, motivated by the higher costs of the vehicles and their added-value related businesses, such as logistics, freight, and transportation management. This paper presents a data-driven and unsupervised methodology to provide a descriptive model assessing the residual value estimates of heavy trucks subject to buy-back. A huge amount of telematics data characterizing the actual usage of commercial vehicles is jointly analyzed with different external conditions (e.g., altimetry), affecting the truck's performance to estimate the devaluation of the vehicle at the buy-back. The proposed approach has been evaluated on a large set of real-world heavy trucks to demonstrate its effectiveness in correctly assessing the real status of wear and residual value at the end of leasing contracts, to provide a few and quantitative insights through an informative, interactive and user-friendly dashboard to make a proper decision on the next business strategies to be adopted. The proposed solution has already been deployed by a private company within its data analytics services since (1) an interpretable descriptive model of the main factors/parameters and corresponding weights affecting the residual value is provided and (2) the experimental results confirmed the promising outcomes of the proposed data-driven methodology

A data-driven energy platform: from energy performance certificates to human-readable knowledge through dynamic high-resolution geospatial maps

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings’ energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process

Feasibility study of the AOSTA experimental campaign

The reduction of the nuclear waste is one of the most important nuclear issues. The high radiotoxicity of the spent fuel is due to plutonium and some minor actinides (MAs) such as neptunium, americium and curium, above all. One way to reduce their hazard is to destroy by fission MAs in appropriate nuclear reactors. To allow the MAs destruction an important effort have been done on the nuclear data due to the poor knowledge in this field. In the framework of one of the NEA Expert Group on Integral Experiments for Minor Actinide Management an analysis of the feasibility of MAs irradiation campaign in the TAPIRO fast research reactor is carried out. This paper provides preliminary results obtained by calculations modelling the irradiation, in different TAPIRO irradiation channels, of some CEA samples coming from the French experimental campaign OSMOSE, loaded with different contents of MAs, in order to access, through particular peak spectrometry, to their capture cross section. On the basis of neutron transport calculation results, obtained by both deterministic and Monte Carlo methods, an estimate of the irradiated samples counting levels from the AOSTA (Activation of OSMOSE Samples in TAPIRO) experimental campaign is provided

Feasibility study of the AOSTA experimental campaign

The reduction of the nuclear waste is one of the most important nuclear issues. The high radiotoxicity of the spent fuel is due to plutonium and some minor actinides (MAs) such as neptunium, americium and curium, above all. One way to reduce their hazard is to destroy by fission MAs in appropriate nuclear reactors. To allow the MAs destruction an important effort have been done on the nuclear data due to the poor knowledge in this field. In the framework of one of the NEA Expert Group on Integral Experiments for Minor Actinide Management an analysis of the feasibility of MAs irradiation campaign in the TAPIRO fast research reactor is carried out. This paper provides preliminary results obtained by calculations modelling the irradiation, in different TAPIRO irradiation channels, of some CEA samples coming from the French experimental campaign OSMOSE, loaded with different contents of MAs, in order to access, through particular peak spectrometry, to their capture cross section. On the basis of neutron transport calculation results, obtained by both deterministic and Monte Carlo methods, an estimate of the irradiated samples counting levels from the AOSTA (Activation of OSMOSE Samples in TAPIRO) experimental campaign is provided

Enabling predictive analytics for smart manufacturing through an IIoT platform

In the last few years, manufacturing systems are getting gradually transformed into smart factories. In this context, an increasing number of information and communication technologies is incorporated towards facilitating management, production, and control processes. The introduction of advanced embedded systems with enhanced connectivity produces a vast amount of data, posing a challenge in terms of data analytics. However, the in-time collection and analysis of acquired data can create insight into the manufacturing process as well as its assets. One aspect of major importance for every production system is preserving its equipment in operational condition, and within those limits that could minimize unplanned breakdowns and production stoppages. This paper details the predictive analytics methodology integrated into the SERENA platform able to: (i) streamline the prognostics of the industrial components, (ii) characterize the health status of the monitored equipment, (iii) generate an early warning related to the condition of the equipment, and (iv) forecast the future evolution of the monitored equipment's degradation. To demonstrate the effectiveness of the proposed methodology, different use cases are discussed with results obtained on real-data collected in real-time from the industrial environments. Copyright (C) 2020 The Authors