In modern Industry 4.0 applications, a huge amount of data is acquired during manufacturing processes and often contaminated with outliers, which can seriously reduce the performance control charting procedures, especially in complex high-dimensional settings. context profile monitoring, we propose new framework that referred to as robust multivariate functional chart (RoMFCC) monitor quality characteristic while being both casewise componentwise outliers. former case, observations are all...

Modern statistical process monitoring (SPM) applications focus on profile monitoring, i.e., the of quality characteristics that can be modeled as profiles, also known functional data. Despite large interest in literature, there is still a lack software to facilitate its practical application. This article introduces funcharts R package implements recent developments SPM multivariate characteristics, possibly adjusted by influence additional variables, referred covariates. The real-time...

Abstract The new EU Regulation urges shipping operators to set up systems for the monitoring, reporting, and verification of CO 2 emissions. Indeed, monitoring data acquisition installed on modern ships have brought a navigation overload that needs be correctly handled make proper decisions about their operation. However, in today's market, there is no standard solution or method available can robustly adopted real environments industry. In view novel attempts solving this issue proposed by...

Abstract To respond to the compelling air pollution programs, shipping companies are nowadays setting‐up on their fleets modern multisensor systems that stream massive amounts of observational data, which can be considered as varying over a continuous domain. Motivated by this context, novel procedure is proposed, extends classical multivariate techniques monitoring functional data and scalar quality characteristic related them. The proposed shown also applicable in real time illustrated...

In many modern industrial scenarios, measurements of the quality characteristics interest are often required to be represented as functional data or profiles. This motivates growing in extending traditional univariate statistical process monitoring (SPM) schemes setting. article proposes a new SPM scheme, which is referred adaptive multivariate EWMA (AMFEWMA), extend well-known exponentially weighted moving average (EWMA) control chart from scalar The proposed method distinguishes itself by...

Abstract In the automotive industry, quality assessment of resistance spot welding (RSW) joints metal sheets is typically based on costly and lengthy offline tests, which are unfeasible in full‐scale production a large scale. However, massive industrial digitalization triggered by Industry 4.0 framework makes online measurements RSW process parameters available for every joint produced. Among these, so‐called dynamic curve (DRC) recognized as full technological signature welds. Motivated...

Abstract Shipping companies are forced by the current EU regulation to set up a system for monitoring, reporting, and verification of harmful emissions from their fleet. In this regulatory background, data collected onboard sensors can be utilized assess ship's operating conditions quantify its CO 2 emission levels. The standard approach analyzing such sets is based on summarizing measurements obtained during given voyage average value. However, compression step may lead significant...

Abstract On modern ships, the quick development in data acquisition technologies is producing data‐rich environments where variable measurements are continuously streamed and stored during navigation thus can be naturally modelled as functional or profiles. Then, both CO emissions (i.e. quality characteristic of interest) profiles that have an impact on them covariates) called to explored light new worldwide European regulations monitoring, reporting verification harmful emissions. In this...

Future grid management systems will coordinate distributed production and storage resources to manage, in a cost effective fashion, the increased load variability brought by electrification of transportation higher share weather dependent production. Electricity demand forecasts at low level aggregation be key inputs for such systems. We focus on forecasting individual household level, which is more challenging than aggregate demand, due lower signal-to-noise ratio heterogeneity consumption...

Abstract Shipping operators are nowadays facing the challenge of monitoring ship performance based on operational data. This is triggered by compelling air pollution regulation EU 2015/757 European Parliament, which aims from January 2018 to monitoring, reporting, and verification all harmful emissions ships operating in Economic Area. On other hand, continuous acquisition data, performed most modern ships, urgently calls for application new opportune statistical methods able deal with...

The shipping industry relies on ship fuel-speed curves to describe the fuel consumption (and CO2 emissions levels) per hour as a function only of vessel’s speed over ground, based dedicated test data. However, they are affected by additional factors in real cases. In this article, novel method is developed elaborating orthogonal least-squares partial (LS-PLS) approach enhance accuracy when information available from multi-sensor systems. Through data examples, shown capable detecting...

In many modern industrial scenarios, the measurements of quality characteristics interest are often required to be represented as functional data or profiles. This motivates growing in extending traditional univariate statistical process monitoring (SPM) schemes setting. article proposes a new SPM scheme, which is referred adaptive multivariate EWMA (AMFEWMA), extend well-known exponentially weighted moving average (EWMA) control chart from scalar The favorable performance AMFEWMA over...

Industrial applications often exhibit multiple in-control patterns due to varying operating conditions, which makes a single functional linear model (FLM) inadequate capture the complexity of true relationship between quality characteristic and covariates, gives rise multimode profile monitoring problem. This issue is clearly illustrated in resistance spot welding (RSW) process automotive industry, where different conditions lead states. In these states, factors such as electrode tip wear...

Statistical process monitoring (SPM) methods are essential tools in quality management to check the stability of industrial processes, i.e., dynamically classify state as control (IC), under normal operating conditions, or out (OC), otherwise. Traditional SPM based on unsupervised approaches, which popular because most applications true OC states not explicitly known. This hampered development supervised that could instead take advantage data containing labels state, although they still need...

Abstract Sensing networks provide nowadays massive amounts of data that in many applications information about curves, surfaces and vary over a continuum, usually time, thus, can be suitably modelled as functional data. Their proper modelling by means analysis approaches naturally addresses new challenges also arising the statistical process monitoring (SPM). Motivated an industrial application, objective present paper is to reader with very transparent set steps for SPM real-world case...