scholarly journals Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

2021 ◽  
Author(s):  
Ensieh Iranmehr ◽  
Ricardo Ferreira ◽  
Tim Böhnert ◽  
Paulo Freitas
Keyword(s):  
Real Data ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Additional Costs ◽  
Pattern Recognition Approach ◽  
Industrial Maintenance ◽  
Anomaly Score ◽  
The Magnetic Field ◽  
Domain Information

Coming up with a system for early detection of machine damages and failures is one of the important challenges in the industrial maintenance procedure to avoid additional costs and downtimes. To approach this goal, this paper uses the signal gathered by a sensing system which employed a spintropic sensor to measure the magnetic field around the machine which somehow shows the machine's behaviour. Using this signal and focusing on analysing and processing the signal, this paper develops a data-driven method to recognize signal patterns and subsequently detects anomalies. A challenging task that we succeeded to overcome in this paper is recognizing relevant signal patterns without having any prior knowledge. An algorithm designed for this task is therefore completely unsupervised which makes it consistent and suitable to apply it for the signals gathered for other types of machines. Using both frequency and time domain information, the proposed algorithm, which utilizes signal processing and machine learning techniques, is able to efficiently identify relevant signal patterns. Clustering results on the real data gathered by the aforementioned sensor have shown the high accuracy of 99.38% in recognizing patterns. Furthermore, an anomaly score measure is used and according to its distribution, anomalies are detected appropriately. <br>

scholarly journals Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics

2020 ◽  
Vol 5 (4) ◽  
pp. 29
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Gas Turbine ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Flame Tube ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Data Driven Modeling ◽  
Engine Power

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public

2017 ◽  
Vol 29 (2) ◽  
pp. 190-209 ◽  
Author(s):  
Jennifer Helsby ◽  
Samuel Carton ◽  
Kenneth Joseph ◽  
Ayesha Mahmud ◽  
Youngsoo Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Police Department ◽  
Data Sources ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Police Legitimacy ◽  
The Public ◽  
Learning Techniques ◽  
Improve Accuracy

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Blue Skies: A Methodology for Data-Driven Clear Sky Modelling

2017 ◽  
Author(s):  
Kartik Palani ◽  
Ramachandra Kota ◽  
Amar Prakash Azad ◽  
Vijay Arya
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Weather Conditions ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Clear Sky ◽  
Learning Techniques ◽  
Atmospheric Data ◽  
Average Irradiance ◽  
Photo Voltaic

One of the major challenges confronting the widespread adoption of solar energy is the uncertainty of production. The energy generated by photo-voltaic systems is a function of the received solar irradiance which varies due to atmospheric and weather conditions. A key component required for forecasting irradiance accurately is the clear sky model which estimates the average irradiance at a location at a given time in the absence of clouds. Current methods for modelling clear sky irradiance are either inaccurate or require extensive atmospheric data, which tends to vary with location and is often unavailable. In this paper, we present a data-driven methodology, Blue Skies, for modelling clear sky irradiance solely based on historical irradiance measurements. Using machine learning techniques, Blue Skies is able to generate clear sky models that are more accurate spatio-temporally compared to the state of the art, reducing errors by almost 50%.

scholarly journals Data-driven yield projections suggest large opportunities to improve Europe’s soybean self-sufficiency under climate change

2020 ◽  
Author(s):  
Nicolas Guilpart ◽  
Toshichika Iizumi ◽  
David Makowski
Keyword(s):  
Climatic Conditions ◽  
Future Climate ◽  
Environmental Benefits ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Soybean Production ◽  
Self Sufficiency ◽  
Learning Techniques ◽  
Rcp 8.5 ◽  
Cultivated Species

AbstractCurrently, demand for soybean in Europe is mostly fulfilled by imports. However, soybean-growing areas across Europe have been rapidly increasing in response to a rising demand for locally-produced, non-GM soybean in recent years. This raises questions about the suitability of European agro-climatic conditions for soybean production. We used data-driven relationships between climate and soybean yield derived from machine-learning techniques to make yield projections under current and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. Results suggest that a self-sufficiency level of 50% (100%) would be achievable in Europe under historical and future climate if 4-5% (9-12%) of the current European cropland is dedicated to soybean production. The associated increase in soybean area in Europe would bring environmental benefits, with a potential decrease of nitrogen fertilizer use in Europe by 5-8% (13-18%) and a possible reduction of deforestation in biodiversity hotspots in South America. However, it would also lead to an important reduction in the production of other cultivated species in Europe (e.g. cereals) and a potential increase in the use of irrigation water.

Analysis of Intrusion Detection and Classification using Machine Learning Approaches

2017 ◽  
Vol 3 (10) ◽  
Author(s):  
Anjum Khan ◽  
Anjana Nigam
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Detection System ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
High Detection Rate ◽  
Learning Techniques ◽  
Result Analysis

 As the network primarily based applications are growing quickly, the network security mechanisms need a lot of attention to enhance speed and preciseness. The ever evolving new intrusion types cause a significant threat to network security. Though varied network security tools are developed, however the quick growth of intrusive activities continues to be a significant issue. Intrusion detection systems (IDSs) are wont to detect intrusive activities on the network. Analysis showed that application of machine learning techniques in intrusion detection might reach high detection rate. Machine learning and classification algorithms facilitate to design “Intrusion Detection Models” which might classify the network traffic into intrusive or traditional traffic. This paper discusses some usually used machine learning techniques in Intrusion Detection System and conjointly reviews a number of the prevailing machine learning IDS proposed by researchers at different times. in this paper an experimental analysis is performed to demonstrate the performance analysis of some existing techniques in order that they will be used further in developing Hybrid Classifier for real data packets classification. The given result analysis shows that KNN, RF and SVM performs best for NSL-KDD dataset.

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