Predicting chattering alarms: A machine Learning approach

Abstract Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models

A data-driven approach to improve control room operators' response

Digitalization has significantly improved productivity and efficiency within the chemical industry. Distributed Control Systems and extensive use of sensor networks enable advanced control strategies and increase optimization opportunities. On the other hand, chemical plants are increasingly complex, equipment is highly interlinked, and it is more difficult to describe the system dynamics through first principles. Finding the root causes of process upsets and predicting dangerous deviations in process conditions is often challenging. Advanced and dynamic tools are needed to grant safe and stable operations in such a complex and multivariate environment. In this context, Machine Learning techniques may be used to exploit and retrieve knowledge from the large amount of data that chemical plants produce and store on a daily basis. Data-driven methods may be adopted to develop predictive models and support a proactive approach to process safety. The study aims to develop Machine Learning techniques to improve the response of control room operators during critical events. Specifically, alarm data originated in an upper-tier Seveso site have been collected, cleaned, and analyzed to identify periods of intense alarm activity. Alarm behavior following operator responses has been evaluated to assess whether the actions were adequate to prevent future alarm occurrences. In doing so, alarm events that reoccur within 30 minutes after an operator acknowledgment have been identified and labeled. Subsequently, a hybrid classification algorithm was trained to predict the probability that a critical alarm reoccurs after being acknowledged by the operator. This predictive tool might be used to support the operator's decision-making process and focus his/her attention on critical alarms that are more likely to occur again in the near future

Behavioral, morphological, and genomic analyses of population structure in brood parasitic indigobirds (Vidua spp.)

The African indigobirds (Vidua spp.) are exceptional among avian brood parasites in that mimicry of host vocalizations plays an integral role in their social behaviors and evolutionary history. Young indigobirds imprint on the vocalizations of their hosts during development, adult males include mimicry of these vocalizations in their own repertoire, and adult females use these songs to choose both their mates and the nests they parasitize. Imprinting on the host during development therefore results in assortative mating and host fidelity, but also provides a mechanism for rapid, sympatric speciation via host shift. Host shifts require some degree of host infidelity, however, and the same behavioral mechanisms may thus lead to hybridization if eggs are laid in the nest of a host species already "occupied" by another indigobird species. Thus, it is not clear if the morphological and genetic similarity of most indigobird species is due to recent common ancestry or ongoing hybridization. I addressed this uncertainty by studying indigobirds in East Africa, a region that was colonized by West African ancestors in the late Pleistocene and is currently home to four indigobird species. I analyzed variation among species in: vi1) the responses of territorial males to playbacks of conspecific and heterospecific vocalizations; 2) temporal and frequency traits of chatter calls and complex non-mimicry songs; 3) morphological characters; and 4) genomic polymorphisms. The playback experiment shows that host mimicry is an important cue in species recognition, and suggests that it may contribute to species cohesion when juveniles or adults disperse beyond the boundaries of their dialect neighborhood. Analyses of both non-mimetic vocalizations and morphological characters (i.e., plumage color and body size) reveal that they are shaped by divergence among species as well as local ecology. Analyses of thousands of "double-digest" restriction site-associated DNA (ddRAD) loci scattered across the genome indicate that both species identity and geographic divergence contribute to population structure. Taken together, the results show that the tempo of speciation and morphological divergence among indigobirds associated with different hosts is likely variable, depending on geographic context, and the breeding ecology and morphology of alternative hosts

Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19

Quantifying the characteristics of public attention is an essential prerequisite for appropriate crisis management during severe events such as pandemics. For this purpose, we propose language-agnostic tweet representations to perform large-scale Twitter discourse classification with machine learning. Our analysis on more than 26 million COVID-19 tweets shows that large-scale surveillance of public discourse is feasible with computationally lightweight classifiers by out-of-the-box utilization of these representations.Comment: 14 pages, 4 figure

Launch COLA Operations: An Examination of Data Products, Procedures, and Thresholds

NASA GSFC and KSC, acting in response to headquarters NASA direction, performed a year-long study of launch collision avoidance (LCOLA) operations in order to determine and recommend best risk assessment and mitigation practices. The following condenses the findings and recommendations of the study into one short summary, a more expanded version of which appears as Section 10

A study of the Pycnonotus bulbul species complex in Southern Africa

The three Pycnonotus bulbuls endemic to Africa, P. barbatus, P. nigricans and P. capensis, occupy mutually exclusive distributions in southern Africa. These species are closely related and appear to occupy very similar ecological niches, only in different regions. Using a multifaceted approach, this study attempts to explain the ecology of this species complex. All three species show similar physiological responses to temperature extremes, and are therefore unlikely to be directly limited by environmental temperature. However, their distributions are highly correlated to a complex of environmental variables, particularly winter minimum temperatures, the coefficient of variation in mean annual rainfall, and the seasonality of rainfall. This combination of environmental parameters can be used to predict the distributions of at least one of the species, P. nigricans, accurately. An analysis of the vocalizations and behaviour of the three species revealed that, whilst P. capensis has a number of recognizably different vocalizations, those of P. barbatus and P. nigricans are very similar. The three have nearly identical behaviours, particularly courtship and pre-copulatory behaviours. The mate recognition systems of the three are therefore extremely similar. P. barbatus is territorial during the breeding season, and exhibits highly structured-variation in male territorial song at the level of the local neighbourhood. The literature pertaining to song dialects is reviewed, and a new hypothesis is postulated to explain song-matching in terms of neighbour/stranger discrimination and the possible existence of cooperative territory defence. A survey of the eastern Cape region, where all three species come into contact, showed that extensive hybridization is taking place between each species pair. Phenotypically, this hybridization is restricted to narrow hybrid zones, that are considered to be stable in both time and place. The evolutionary and ecological significance of these zones to the distributions of the species is discussed, and it is proposed that the zones are maintained by selection acting on differentially-adapted genomes along an environmental gradient

Profile monitoring via sensor fusion: The use of PCA methods for multi-channel data

Continuous advances of sensor technology and real-time computational capability are leading to data-rich environments to improve industrial automation and machine intelligence. When multiple signals are acquired from different sources (i.e. multi-channel signal data), two main issues must be faced: (i) the reduction of data dimensionality to make the overall signal analysis system efficient and actually applicable in industrial environments, and (ii) the fusion of all the sensor outputs to achieve a better comprehension of the process. In this frame, multi-way principal component analysis (PCA) represents a multivariate technique to perform both the tasks. The paper investigates two main multi-way extensions of the traditional PCA to deal with multi-channel signals, one based on unfolding the original data-set, and one based on multi-linear analysis of data in their tensorial form. The approaches proposed for data modelling are combined with appropriate control charting to achieve multi-channel profile data monitoring. The developed methodologies are demonstrated with both simulated and real data. The real data come from an industrial sensor fusion application in waterjet cutting, where different signals are monitored to detect faults affecting the most critical machine components

Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills

Der hier vorgestellte Ansatz ist in der Lage, zwei spezifische schwere Fehler zu erkennen, sie zu identifizieren, zwischen vier verschiedenen Systemzuständen zu unterscheiden und eine Prognose bezüglich des Systemverhaltens zu geben. Die vorliegende Arbeit untersucht die Zustandsüberwachung des komplexen Herstellungsprozesses eines Warmbandwalzwerks. Eine signalbasierte Fehlerdiagnose und ein Fehlerprognoseansatz für den Bandlauf werden entwickelt. Eine Literaturübersicht gibt einen Überblick über die bisherige Forschung zu verwandten Themen. Es wird gezeigt, dass die große Anzahl vorheriger Arbeiten diese Thematik nicht gelöst hat und dass weitere Untersuchungen erforderlich sind, um eine zufriedenstellende Lösung der behandelten Probleme zu erhalten. Die Entwicklung einer neuen Signalverarbeitungskette und die Signalverarbeitungsschritte sind detailliert dargestellt. Die Klassifikationsaufgabe wird in Fehlerdiagnose, Fehleridentifikation und Fehlerprognose differenziert. Der vorgeschlagene Ansatz kombiniert fünf verschiedene Methoden zur Merkmalsextraktion, nämlich Short-Time Fourier Transformation, kontinuierliche Wavelet Transformation, diskrete Wavelet Transformation, Wigner-Ville Distribution und Empirical Mode Decomposition, mit zwei verschiedenen Klassifikationsalgorithmen, nämlich Support-Vektor Maschine und eine Variation der Kreuzkorrelation, wobei letztere in dieser Arbeit entwickelt wurde. Kombinationen dieser Merkmalsextraktion und Klassifikationsverfahren werden an Walzkraft-Daten aus einer Warmbreitbandstraße angewendet.The approach introduced here is able to detect two specific severe faults, to identify them, to distinguish between four different system states, and to give a prognosis on the system behavior. The presented work investigates the condition monitoring of the complex production process of a hot strip rolling mill. A signal-based fault diagnosis and fault prognosis approach for strip travel is developed. A literature review gives an overview about previous research on related topics. It is shown that the great amount of previous work does not cope with the problems treated in this work and that further investigation is necessary to provide a satisfactory solution. The design of a new signal processing chain is presented and the signal processing steps are detailed. The classification task is differentiated into fault detection, fault identification and fault prognosis. The proposed approach combines five different methods for feature extraction, namely short time Fourier transform, continuous wavelet transform, discrete wavelet transform, Wigner-Ville distribution, and empirical mode decomposition, with two different classification algorithms, namely support vector machine and a variation of cross-correlation, the latter developed in this work. Combinations of these feature extraction and classification methods are applied to rolling force data originating from a hot strip mill

Una nueva capa de protección a través de súper alarmas con capacidad de diagnóstico

An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms furthermore, a case study of the petrochemical sector using CBAM is presented to build the chronicles of the normal startup, abnormal start-up, and normal shutdown scenarios. Finally, the scenario validation is performed for an abnormal start-up, showing how a super-alarm is generated.Se puede formular una metodología de gestión de alarmas como un problema de reconocimiento de secuencia de eventos discretos en el que se utilizan patrones de tiempo para identificar la condición segura del proceso, especialmente en las etapas de arranque y parada de planta. Las plantas industriales, particularmente en las industrias petroquímica, energética y química, requieren una administración combinada de todos los eventos que pueden producir un accidente catastrófico. En este documento, se introduce una nueva capa de protección (súper alarma) a los procesos industriales basados en una etapa de diagnóstico. Las alarmas y las acciones estándar del procedimiento operativo son asumidas como eventos discretos involucrados en las secuencias, luego la etapa de diagnóstico corresponde al reconocimiento de la situación cuando ocurren estas secuencias. Esto proporciona a los operadores información pertinente sobre las situaciones normales o anormales inducidas por el flujo de alarmas. La gestión de alarmas basadas en crónicas (CBAM) es la metodología utilizada en este artículo para construir las crónicas que permitirán generar las super alarmas, además, se presenta un caso de estudio del sector petroquímico que usa CBAM para construir las crónicas de los escenarios de un arranque normal, un arranque anormal y un apagado normal. Finalmente, la validación del escenario se realiza para un arranque anormal, mostrando cómo se genera una súper alarma