1,720,995 research outputs found
Orthogonal LS-PLS approach to ship fuel-speed curves for supporting decisions based on operational data
No full textThe shipping industry relies on ship fuel-speed curves to describe the fuel consumption (and CO2 emissions levels) per hour as a function only of the vessel’s speed over ground, based on dedicated test data. However, they are affected by additional factors in real cases. In this article, a novel method is developed elaborating the orthogonal least-squares partial least-squares (LS-PLS) approach to enhance fuel-speed curves accuracy when information is available on additional factors from multi-sensor systems. Through real data examples, the approach is shown capable of detecting anomalies in CO2 emission levels and testing the effectiveness of ship energy efficiency initiatives
Introduzione ai modelli di analisi multivariata a variabili latenti. Applicazioni per l’ingegneria.
No full text
Analysis of profiles for monitoring of modern ship performance via partial least squares methods
No full textShipping operators are nowadays facing the challenge of monitoring ship performance based on operational data. This is triggered by the compelling air pollution regulation EU 2015/757 of the European Parliament, which aims from January 2018 to monitoring, reporting, and verification of all harmful emissions of ships operating in the European Economic Area. On the other hand, the continuous acquisition of operational data, which is performed on most of the modern ships, urgently calls for the application of new and opportune statistical methods able to deal with high-dimensional data. Ship operating conditions can be in fact described by sensor signals collected throughout each voyage and stored as profiles. In this paper, the latter are analyzed through multiway partial least squares regression of the average fuel consumption per hour over each voyage, which is chosen as scalar performance response, being proportional to harmful emissions. The proposed approach is able to monitor profiles with different length at different voyages. Nevertheless, it is capable of indicating at which instant anomalies may have occurred in ship operating conditions. The proposed approach is shown to be able to furnish clear indications for supporting prognosis of faults. By means of real data acquired from a Ro-Pax cruise ship owned by the shipping company Grimaldi Group, a different multilinear version that explicitly takes into account the 3-way structure of the data is also compared with the proposed approach
An adaptive multivariate functional EWMA control chart
Full text linkIn many modern industrial scenarios, measurements of the quality characteristics of interest are often required to be represented as functional data or profiles. This motivates the grow-ing interest in extending traditional univariate statistical process monitoring (SPM) schemes to the functional data setting. This article proposes a new SPM scheme, which is referred to as adaptive multivariate functional EWMA (AMFEWMA), to extend the well-known exponentially weighted moving average (EWMA) control chart from the univariate scalar to the multivariate functional setting. The proposed method distinguishes itself by adaptively selecting the weighting parameter in the calculation of the EWMA statistic to enhance the sensitivity of the AMFEWMA control chart across a spectrum of potential out-of-control scenarios. Such adaptability is essential in industrial processes, where multivariate functional quality characteristics are also subject to varying degrees of change. The favorable performance of the AMFEWMA control chart over existing methods is assessed via an extensive Monte Carlo simulation. Its practical applicability is demonstrated through a case study in monitoring the quality of a resistance spot welding (RSW) process in the automotive industry through online observations of dynamic resistance curves, which are associated with multiple spot welds on the same car body and are recognized as highly representative of the RSW process quality. The proposed method is implemented in the R package funcharts, available online on CRAN
Robust Multivariate Functional Control Chart
Full text linkIn modern Industry 4.0 applications, a huge amount of data is acquired during
manufacturing processes that are often contaminated with anomalous observations
in the form of both casewise and cellwise outliers. These can seriously reduce
the performance of control charting procedures, especially in complex and
high-dimensional settings. To mitigate this issue in the context of profile
monitoring, we propose a new framework, referred to as robust multivariate
functional control chart (RoMFCC), that is able to monitor multivariate
functional data while being robust to both functional casewise and cellwise
outliers. The RoMFCC relies on four main elements: (I) a functional univariate
filter to identify functional cellwise outliers to be replaced by missing
components; (II) a robust multivariate functional data imputation method of
missing values; (III) a casewise robust dimensionality reduction; (IV) a
monitoring strategy for the multivariate functional quality characteristic. An
extensive Monte Carlo simulation study is performed to compare the RoMFCC with
competing monitoring schemes already appeared in the literature. Finally, a
motivating real-case study is presented where the proposed framework is used to
monitor a resistance spot welding process in the automotive industry
Functional clustering methods for resistance spot welding process data in the automotive industry
No full textAnalysis of resistance of spot welding process data in the automotive industry via functional clustering techniques
No full text- …
