Robust FDI/FTC using Set-membership Methods and Application to Real Case Studies

This paper reviews the use of set-membership methods in robust fault detection and isolation (FDI) and tolerant control (FTC). Set-membership methods use a deterministic unknown-but-bounded description of noise and parametric uncertainty (interval models). These methods aims to check the consistency between observed and predicted behavior by using simple sets to approximate the set of possible behaviors (in parameter or state space). When an inconsistency is detected a fault can be indicated, otherwise nothing can be stated. The same principle can be used to identify interval models for fault detection and to develop methods for fault tolerance evaluation. Finally, some real application of these methods will end the paper exemplifying the success of these methods in FDI/FTC.Postprint (published version

Nonparametric monitoring of sunspot number observations: a case study

Solar activity is an important driver of long-term climate trends and must be accounted for in climate models. Unfortunately, direct measurements of this quantity over long periods do not exist. The only observation related to solar activity whose records reach back to the seventeenth century are sunspots. Surprisingly, determining the number of sunspots consistently over time has remained until today a challenging statistical problem. It arises from the need of consolidating data from multiple observing stations around the world in a context of low signal-to-noise ratios, non-stationarity, missing data, non-standard distributions and many kinds of errors. The data from some stations experience therefore severe and various deviations over time. In this paper, we propose the first systematic and thorough statistical approach for monitoring these complex and important series. It consists of three steps essential for successful treatment of the data: smoothing on multiple timescales, monitoring using block bootstrap calibrated CUSUM charts and classifying of out-of-control situations by support vector techniques. This approach allows us to detect a wide range of anomalies (such as sudden jumps or more progressive drifts), unseen in previous analyses. It helps us to identify the causes of major deviations, which are often observer or equipment related. Their detection and identification will contribute to improve future observations. Their elimination or correction in past data will lead to a more precise reconstruction of the world reference index for solar activity: the International Sunspot Number.Comment: 27 pages (without appendices), 6 figure

AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described

Adaptive model-based battery management - Predicting energy and power capability

The battery is the limiting system for automotive electrication due to cost, size, and uncertain degradation. To be competitive the battery must therefore be used optimally. This thesis address the on-line battery management problem, with primary objectives to:(i) enable optimal usage of the battery by providing accurate estimates of its power and energy capability, while (ii) ensuring durability by keeping the battery inside predefined operating limits at all times. This means translating measurable information of current, voltage, and temperature into cell related quantities such as state-of-charge (SOC) and state-of health (SOH), and vehicle related quantities such as power capability and available energy.The main difficulty of battery management is that battery cells have complex, non-linear dynamics that changes with both operating conditions, usage history, and age. This thesis and he appended papers proposes a system of adaptive algorithms for on-line battery estimation. Several aspects are considered, from modelling and parameter estimation to estimation of SOC, energy, and power. Recursive algorithms are proposed for estimation of parameters and SOC, while power and energy are estimated using algebraic expressions derived from equivalent circuit battery models. The algorithms are evaluated on lithium-ion battery cell data collected laboratory tests. For the cell chemistries considered, the evaluation indicates that accuracy within 2% can be achieved for both SOC and power, also in cases with limited prior information about the cell

In-service diagnostics for wire-bond lift-off and solder fatigue of power semiconductor packages

Wire-bond lift-off and Solder fatigue are degradation mechanisms that dominate the lifetime of power semiconductor packages. Although their lifetime is commonly estimated at the design stage, based on mission profiles and Physics-of-Failure models, there are many uncertainties associated with such lifetime estimates, emerging, for example, from model calibration errors, manufacturing tolerances, etc. These uncertainties, combined with the diverse working environments of power semiconductor packages result in inaccurate lifetime estimates. This paper presents an approach for estimating the extent of degradation in power semiconductor packages based on online monitoring of key parameters of the semiconductor, namely, the thermal resistance Rthja and the electrical resistance RCE. Using these two parameters, solder fatigue and wire-bond lift-off can be detected during normal converter operation. In order to estimate these two parameters, two techniques are introduced: a residual obtained from a Kalman filter which estimates the change in the thermal resistance Rthja and a Recursive Least-Squares (RLS) algorithm which is used to estimate the electrical resistance. Both methods are implemented online and validated experimentally

Distinctive Frontal and Occipitotemporal Surface Features in Neglectful Parenting.

Although the brain signatures of adaptive human parenting are well documented, the cortical features associated with maladaptive caregiving are underexplored. We investigated whether cortical thickness and surface area vary in a small group of mothers who had neglected their children (24 in the neglect group, NG) compared to a control group of mothers with non-neglectful caregiving (21 in the control group, CG). We also tested whether the cortical differences were related to dyadic mother-child emotional availability (EA) in a play task with their children and whether alexithymia involving low emotional awareness that characterizes the NG could play a role in the cortical-EA associations. Whole-brain analysis of the cortical mantle identified reduced cortical thickness in the right rostral middle frontal gyrus and an increased surface area in the right lingual and lateral occipital cortices for the NG with respect to the CG. Follow-up path analysis showed direct effects of the right rostral middle frontal gyrus (RMFG) on the emotional availability (EA) and on the difficulty to identify feelings (alexithymia factor), with a marginal indirect RMFG-EA effect through this factor. These preliminary findings extend existing work by implicating differences in cortical features associated with neglectful parenting and relevant to mother-child interactive bonding

Distinctive Frontal and Occipitotemporal Surface Features in Neglectful Parenting.

Although the brain signatures of adaptive human parenting are well documented, the cortical features associated with maladaptive caregiving are underexplored. We investigated whether cortical thickness and surface area vary in a small group of mothers who had neglected their children (24 in the neglect group, NG) compared to a control group of mothers with non-neglectful caregiving (21 in the control group, CG). We also tested whether the cortical differences were related to dyadic mother-child emotional availability (EA) in a play task with their children and whether alexithymia involving low emotional awareness that characterizes the NG could play a role in the cortical-EA associations. Whole-brain analysis of the cortical mantle identified reduced cortical thickness in the right rostral middle frontal gyrus and an increased surface area in the right lingual and lateral occipital cortices for the NG with respect to the CG. Follow-up path analysis showed direct effects of the right rostral middle frontal gyrus (RMFG) on the emotional availability (EA) and on the difficulty to identify feelings (alexithymia factor), with a marginal indirect RMFG-EA effect through this factor. These preliminary findings extend existing work by implicating differences in cortical features associated with neglectful parenting and relevant to mother-child interactive bonding

Predictive model for the degradation state of a hydraulic system with dimensionality reduction

In recent years, the optimization in the use of resources has a key role in achieving a bigger marginality, reducing the operative costs. Due to the advances in the data science field, even the maintenance context is living important changes. The predictive maintenance and the condition-based maintenance can overcome the classic traditional maintenance methods, like the time-based maintenance or the corrective maintenance, with respect to the first intervention, reducing the costs for unscheduled maintenance, manpower, or loss of production and extending the useful life of the components. Based on these presuppositions, the paper proposes the development of a predictive model for the degradation state of the components of a complex hydraulic system, with some tests and some suggestions about the dimensionality reduction. The system has four known types of breakdown, with different degrees of severity; moreover, a fifth parameter represents whether the cycle has reached stable conditions or not

Distinctive Frontal and Occipitotemporal Surface Features in Neglectful Parenting

Published: 18 March 2021Although the brain signatures of adaptive human parenting are well documented, the cortical features associated with maladaptive caregiving are underexplored. We investigated whether cortical thickness and surface area vary in a small group of mothers who had neglected their children (24 in the neglect group, NG) compared to a control group of mothers with non-neglectful caregiving (21 in the control group, CG). We also tested whether the cortical differences were related to dyadic mother-child emotional availability (EA) in a play task with their children and whether alexithymia involving low emotional awareness that characterizes the NG could play a role in the cortical-EA associations. Whole-brain analysis of the cortical mantle identified reduced cortical thickness in the right rostral middle frontal gyrus and an increased surface area in the right lingual and lateral occipital cortices for the NG with respect to the CG. Follow-up path analysis showed direct effects of the right rostral middle frontal gyrus (RMFG) on the emotional availability (EA) and on the difficulty to identify feelings (alexithymia factor), with a marginal indirect RMFG-EA effect through this factor. These preliminary findings extend existing work by implicating differences in cortical features associated with neglectful parenting and relevant to mother-child interactive bonding.This research was funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, grant number RTI2018‐098149‐B‐I00 to M.J.R. and I.L, and by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska‐Curie Individual Fellowship, grant agreement number 893329 to L.G.P