Computational monitoring in real time: review of methods and applications

Dyskin, Arcady; Başarır, Hakan; Doherty, James; Elchalakani, Mohamed; Joldes, Grand Roman; Karrech, Ali; Lehane, Barry; Miller, Karol; Pasternak, Elena; Shufrin, Igor; Wittek, Adam

doi:10.1007/s40948-018-0086-6

Cited by 20 publications

(10 citation statements)

References 282 publications

(319 reference statements)

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“…The Matlab (MathWorks, Natick, MA, USA) triangulate function is used to obtain the spatial coordinates of a given point in the reference system associated with the left camera of the vision system, based on the position of the point (measurement marker) on a pair of images (Figure 5a) [29][30][31][32]. The resulting grid of points represents the cutting head side surface at the point where the pick holder is mounted.…”

Section: Processing and Analysis Of Measurement Imagesmentioning

confidence: 99%

Validation of a Method for Measuring the Position of Pick Holders on a Robotically Assisted Mining Machine’s Working Unit

Cheluszka

Jagieła-Zając

2022

Energies

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For effective mining, it is essential to ensure that the picks are positioned correctly on the working unit of a mining machine. This is due to the fact that the design of roadheader cutting heads/drums using computer-aided tools is based on the operating conditions of the roadheader/shearer/milling machine. The geometry of the cutting head is optimized for selected criteria by simulating the mining process using a computer. The reclaimed cutting head bodies that are utilized in production are manufactured again in the overhaul process. Ensuring that the dimensions of the cutting head bodies match the rated dimensions is labor-intensive and involves high production costs. For dimensional deviations of the cutting head bodies, it is necessary to control the position of the pick holders relative to the cutting head side surface in real time during robotic-assisted assembly. This article discusses the possibility of utilizing a stereovision system for calculating the distance between the pick holder base and the roadheader cutting head side surface at the point where the pick holder is mounted. The proposed measurement method was tested on a robotic measurement station constructed for the purpose of the study. A mathematical measurement model and procedures that allow automatic positioning of the camera system to the photographed objects, as well as acquisition and analysis of the measurement images, were developed. The proposed method was validated by using it for measuring the position of the pick holders relative to the side surface of the working unit of a mining excavating machine, focusing on its application in robotic technology. The article also includes the results observed in laboratory tests performed on the developed measurement method with an aim of determining its suitability for the metrology task under consideration.

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Section: Processing and Analysis Of Measurement Imagesmentioning

confidence: 99%

Validation of a Method for Measuring the Position of Pick Holders on a Robotically Assisted Mining Machine’s Working Unit

Cheluszka

Jagieła-Zając

2022

Energies

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“…Recently, real-time condition monitoring is becoming very popular in manufacturing, maintenance, and usage of many engineering systems (Dyskin et al, 2018) and civil engineering infrastructures (Chang, Flatau, & Liu, 2003). Computational models have been developed for estimating the properties of constructional materials (Rafiei, Khushefati, Demirboga, & Adeli, 2017), detecting damages to building structures (Rafiei & Adeli, 2017b, 2018a, predicting construction costs (Rafiei & Adeli, 2018b), etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To ensure efficient operation and maintenance, it is important to gather real‐time information about the performance and integrity of engineering systems. This is typically performed through a computational monitoring process that involves observation of a system, analysis of the obtained data, and prediction of future performance (Dyskin et al., 2018). During a disaster, due to disruptions, the performance of critical infrastructures degrades rapidly—leading to cascading failures (Du, Cai, Sun, & Minsker, 2017; Kadri, Birregah, & Châtelet, 2014).…”

Section: Introductionmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the cooccurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption-whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo-parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption-related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co-occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations. 1 INTRODUCTION Cities and communities all over the world largely depend on critical infrastructure systems/services such as electrical power, water distribution, communication services, and transportation networks. The growing interconnectedness and © 2020 Computer-Aided Civil and Infrastructure Engineering interdependency among these systems have changed the organizational and operational factors and increased the vulnerability in the face of unwanted disruptions. These systems provide critical services to a large population, and thus when disrupted they affect our quality of life, local and regional economy, and the overall community wellbeing. The need to

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“…The fault detection technique based on a formal model of the machine to be monitored, it does not depend on the nature of the signal, which provides a good prognosis of the faults in transient and steady states of IM. Moreover, the formal model will serve as a reference for defining the normal operation, and any deviation from the operating point will be a sign of the failure [18].…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

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Computational monitoring in real time: review of methods and applications

Cited by 20 publications

References 282 publications

Validation of a Method for Measuring the Position of Pick Holders on a Robotically Assisted Mining Machine’s Working Unit

Validation of a Method for Measuring the Position of Pick Holders on a Robotically Assisted Mining Machine’s Working Unit

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

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