Practical methods for detecting and removing transient changes in univariate oscillatory time series

Zhou, Baifan; Chioua, Moncef; Schlake, Jan-Christoph

doi:10.1016/j.ifacol.2017.08.997

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…The present work extends the analysis of the transient detection and removal methods introduced by Zhou et al where the authors proposed heuristic methods to detect and to remove transients from oscillatory signals by applying it in conjunction with oscillation and transfer entropy method to study the effects. In addition to this expansion, the procedure is improved and formulated precisely in mathematical terms.…”

Section: Introductionmentioning

confidence: 83%

“…Jelali and Huang claim that oscillation detection methods can nowadays be considered as a largely solved research topic. In industrial practice, however, the presence of transient disturbances in the oscillatory signals can lead to a decrease in the accuracy of standard oscillation detection methods , and therefore reduce their industrial acceptance.…”

Section: Introductionmentioning

confidence: 99%

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Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

Zhou

Chioua²,

Bauer

et al. 2019

Ind. Eng. Chem. Res.

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Oscillations occurring in industrial process plants often reflect the presence of severe disturbances affecting process operations. Accurate detection and root-cause analysis of oscillations is of great interest for the economic viability of the process operation. Standard oscillation detection and root cause analysis methods require a large enough number of data samples. Unrelated transient changes superimposed on the oscillation pattern reduce the number of useful data samples. The present paper proposes simple heuristic methods to effectively detect and remove two types of transient changes from oscillatory signals, namely step changes and spikes. The proposed methods are used to pre-process oscillatory time series. The accuracy gained when using auto-correlation function method for oscillation detection 1 and transfer entropy method for oscillation propagation 2 is experimentally evaluated. The methods are carried out on a 1.3-Butadiene production process where several measurements showed an established oscillation occurring after a production level change.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

Zhou

Chioua²,

Bauer

et al. 2019

Ind. Eng. Chem. Res.

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“…KGs provide semantically structured information that can be interpreted by computing machines [52,67], and an efficient foundation for standardised ways of data retrieval and analytics to support data driven methods. Data driven methods have been widely used in industries [33,34,57,58], especially machine learning [48,56,[63][64][65]. The problem of transforming a bigger ontology to a smaller ontology of the same domain is often referred to as ontology modularisation [4][5][6][7]29] and ontology summarisation [35].…”

Section: Related Workmentioning

confidence: 99%

Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding

Zhou

Chen

et al. 2021

Proceedings of the 10th International Joint Conference on Knowledge Graphs

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Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results. CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Computing methodologies → Knowledge representation and reasoning; Ontology engineering.

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“…Indeed, modern machines and production systems are equipped with sensors that constantly collect and send data and with control units that monitor and process these data, coordinate machines and manufacturing environment and send messages, notifications, requests. Availability of these voluminous data has led to a large growth of interest in data analysis for a wide range of industrial applications [5][6][7][8], especially the use of Machine Learning (ML) approaches for condition monitoring for manufacturing processes, machines, oil, gas and chemical systems, and products by predicting system disturbance, machines' down-times or the quality of manufactured products [9]. Such approaches allow to analyse large amount of data and gain fruitful insights for condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Fig. 17.Radar charts of questionnaires scores on 10 questions(1)(2)(3)(4)(5)(6) and aggregated to 6 dimensions (7-13) defined in Table2. Std is standard deviation.…”

mentioning

confidence: 99%

SemML: Facilitating Development of ML Models for Condition Monitoring with Semantics

Zhou¹,

Svetashova²,

Gusmão³

et al. 2021

SSRN Journal

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Practical methods for detecting and removing transient changes in univariate oscillatory time series

Cited by 13 publications

References 12 publications

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding

SemML: Facilitating Development of ML Models for Condition Monitoring with Semantics

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