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HAZOP, FMECA, monitoring algorithm, and Bayesian network integrated approach for an exhaustive risk assessment and real-time safety analysis: Case study

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PROCESS SAFETY PROGRESS 2024年第4期43卷 784-813页

作者： Nehal, Naima Mekkakia-Mehdi, Mokhtaria Lounis, Zakia Guetarni, Islam H. M. Lounis, Zoubida Univ Mohamed Ben Ahmed Oran 2 Inst Maintenance & Secur Industrielle Lab Ingn Secur Industrielle & Dev Durable Secur Industrielle & Environm Oran 31000 Algeria Ecole Natl Polytech Oran Maurice Audin Lab Materiaux LABMAT Oran Algeria

Hazard studies are essential in the petrochemical industry to ensure safe operations. This article provides an in-depth analysis of the hazards associated with a vacuum distillation unit furnace. This study aims to identify probable hazard scenarios related to furnace operation, assess the associated risks, and provide prevention and mitigation strategies. A comprehensive strategy was employed to achieve these objectives, combining two analysis methods: HAZard OPerability (HAZOP) and Failure Modes, Effects, and Criticality Analysis (FMECA). This integrated approach enables a comprehensive risk assessment to be carried out and appropriate preventive measures to be taken to maintain safe operations, including renovation work. Then, depending on the results of the two methods, it is essential to constantly evaluate equipment safety, taking into account parameters such as furnace efficiency, tube temperature, and fume temperature. Therefore, a monitoring program has been created in Python, which enables real-time examination of the furnace's safety with these critical parameters. If safety conditions are compromised, alarms are sent to mitigate risks, particularly in case of a failure. A Bayesian model is also developed to evaluate the algorithm's results and determine renovation and failure case scenarios. This comprehensive approach improves risk assessment's reliability, precision, maintains safe and efficient industrial operations.

关键词： Bayesian network Hazard operability monitoring algorithm preventive measures renovation works

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A Smart Status Based monitoring algorithm for the Dynamic Analysis of Memory Safety

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第4期33卷 1-47页

作者： Chen, Zhe Yan, Rui Ma, Yingzi Sui, Yulei Xue, Jingling Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Univ New South Wales Sydney Australia Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Ave Nanjing 211106 Jiangsu Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

C is a dominant programming language for implementing system and low-level embedded software. Unfortunately, the unsafe nature of its low-level control of memory often leads to memory errors. Dynamic analysis has been widely used to detect memory errors at runtime. However, existing monitoring algorithms for dynamic analysis are not yet satisfactory, as they cannot deterministically and completely detect some types of errors, such as segment confusion errors, sub-object overflows, use-after-frees and memory leaks. We propose a new monitoring algorithm, namely SMATUS, short for smart status, that improves memory safety by performing comprehensive dynamic analysis. The key innovation is to maintain at runtime a small status node for each memory object. A status node records the status value and reference count of an object, where the status value denotes the liveness and segment type of this object, and the reference count tracks the number of pointer variables pointing to this object. SMATUS maintains at runtime a pointer metadata for each pointer variable, to record not only the base and bound of a pointer's referent but also the address of the referent's status node. All the pointers pointing to the same referent share the same status node in their pointer metadata. A status node is smart in the sense that it is automatically deleted when it becomes useless (indicated by its reference count reaching zero). To the best of our knowledge, SMATUS represents the most comprehensive approach of its kind. We have evaluated SMATUS by using a large set of programs including the NIST Software Assurance Reference Dataset, MSBench, MiBench, SPEC and stress testing benchmarks. In terms of effectiveness (detecting different types ofmemory errors), SMATUS outperforms state-of-the-art tools, Google's AddressSanitizer, SoftBoundCETS and Valgrind, as it is capable of detecting more errors. In terms of performance (the time and memory overheads), SMATUS outperforms SoftBoundCETS and V

关键词： Software quality software testing dynamic analysis bug finding memory errors monitoring algorithm

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AI Tools for Media Data Governance in the Post-Truth Era: from Abnormal Data Recognition to Intelligent Opinion monitoring algorithm 5

AI Tools for Media Data Governance in the Post-Truth Era: fr...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： He, Yonghua Peng, Debin Guangzhou Coll Technol & Business Sch Management Guangzhou 510850 Peoples R China Jinan Univ Student Affairs Off Guangzhou 510632 Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

AI tools for media data governance in the post-truth era from abnormal data recognition to intelligent opinion monitoring algorithm is studied in the paper. The decisions and rules made by artificial intelligence are not necessarily superior to human beings. In terms of fairness, machine learning identifies patterns from past data and makes new decisions based on these patterns. Therefore, AI systems may consolidate or amplify historical bias. In the analysis of network public opinion, the first thing is to then grab the network text, and analyze the behavior characteristics, classify different behavior characteristics, so as to detect network public opinion according to some different behavior types and with this inspiration. In our designed model, the data mining algorithm is designed for the modelling. Through the comparison analysis, the performance is then validated.

关键词： AI Tools Media Data Governance Post-Truth Era Data Recognition Intelligent Opinion monitoring algorithm

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monitoring algorithm for Speed Information of Autonomous Vehicles Based on Magnetoresistive Sensor

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JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING 2020年第1期14卷 43-52页

作者： Tang, Yu Panzhihua Univ Sch Intelligent Mfg Panzhihua 617000 Peoples R China

Based on a magnetoresistive sensor, an algorithm for speed information monitoring of autonomous vehicles suitable for fewer target feature points is proposed. When using a magnetoresistive sensor on the road, the monitoring principle of the magnetoresistive sensor is that the magnetoresistive sensor generates a voltage signal and sends it to a signal processing module. In the signal processing module, the vehicles signal is compared with a threshold value to determine whether the vehicle is present. In the signal processing module, the vehicle signal is compared in the presence of the vehicle, and the node time synchronization technology is used to select the two-node autonomous vehicle speed information monitoring method. Then, the speed information of the autonomous vehicles in motion is monitored in real time. The vehicle speed information is sent to the upper node by using the single chip microcomputer. It is then sent to the coordinator module using ZigBee technology in the wireless sensor network. Finally, it is sent to the intelligent traffic monitoring center to achieve speed information monitoring of autonomous vehicles. The experimental results show that the accuracy rate of the autonomous vehicles' speed information monitored by the algorithm is above 97%, and the monitored energy consumption is only 13.5 J. This shows that the algorithm's monitoring accuracy and energy consumption have an advantage. (C) 2020 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved.

关键词： magnetoresistive sensor autonomous vehicles speed information monitoring algorithm

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Video Key Frame monitoring algorithm and Virtual Reality Display Based on Motion Vector

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IEEE ACCESS 2020年 8卷 159027-159038页

作者： Wang, Zhe Zhu, Yan Qufu Normal Univ Dept Phys Educ Rizhao 276800 Peoples R China Qufu Normal Univ Coll Marxism Rizhao 276800 Peoples R China

In this article, a motion vector-based video key frame detection algorithm is proposed to solve the problem of miss election and missing selection caused by the difficulty in detecting the moving target characteristics of the video key frame. Firstly, the entropy of adjacent frame difference and the two-dimensional entropy of image are introduced, and the combination of the two is taken as the measurement of the difference between video frames. Secondly, outliers are detected by statistical tools to obtain the lens boundary, thus realizing the adaptive lens detection of video content. Then, ViBe algorithm is used to detect the foreground object in the video sequence and extract the scale-invariant feature transformation features of the foreground moving object. Finally, the motion vector is introduced, and the sum of the block matching results is motion vector by partitioning the two adjacent frames and performing block matching. The magnitude of motion vector reflects the intensity of motion in the video, so active and inactive motion regions are obtained, and the similarity of video frames is calculated according to the defined formula, and key frames are extracted in these regions respectively. The experimental results show that the detection algorithm proposed in this article improves the video results with rich motion information obviously, and the objective indexes and subjective scores are improved to some extent, which improves the universality of the algorithm. In addition, this article also studies the display mode of key frame extraction results in virtual reality environment. The key frame display mode of video in virtual reality is optimized mainly by changing the display mode of information and changing the scene and testing the result of user task execution.

关键词： Histograms Surveillance Streaming media Virtual reality Lenses Video sequences Feature extraction Video key frame monitoring algorithm virtual reality display motion vector

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Hot-Pressing Furnace Current monitoring and Predictive Maintenance System in Aerospace Applications

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SENSORS 2023年第4期23卷 2230-2230页

作者： Chen, Hong-Ming Zhang, Jia-Hao Wang, Yu-Chieh Chang, Hsiang-Ching King, Jen-Kai Yang, Chao-Tung Tunghai Univ Dept Appl Math 1727 Sec 4 Taiwan Blvd Taichung 407224 Taiwan Tunghai Univ Dept Comp Sci 1727 Sec 4 Taiwan Blvd Taichung 407224 Taiwan Aerosp Ind Dev Corp Innovat R&D Ctr 1-1 Hanxiang Rd Taichung 407803 Taiwan Tunghai Univ Res Ctr Smart Sustainable Circular Econ 1727 Sec 4 Taiwan Blvd Taichung 407224 Taiwan

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater's power consumption and the hot-pressing furnace's fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.

关键词： predictive maintenance current monitoring machine learning monitoring algorithm aerospace

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A monitoring Method for the State of Yarn Based on Sliding Window and Its Application 3

A Monitoring Method for the State of Yarn Based on Sliding W...

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3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)

作者： Zhang, Yu Gu, LiMiao Chu, Weijie Jia, Fang Southeast Univ Dept Mech Engn Nanjing Peoples R China

ISBN: (纸本)9781665441094

This paper aims at the problem of yarn breakage in the spinning affecting workshop safety and production. A general monitoring method for the yarn based on sliding window is proposed. A low-cost monitoring system with AC detection method is designed by taking the spinning traveller as the monitoring object. An improved yarn monitoring algorithm based on sliding window realized the recognition of various abnormal situations of yarn and process. The experiments show the higher accuracy of the proposed yarn monitoring method compared to traditional method, which can effectively realize monitoring of the yarn and ensure the workshop safety and quality of yarn.

关键词： yarn monitoring sliding window monitoring algorithm interpolation method

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Real-time trace of riser profile and stress with numerical inclinometers

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OCEAN ENGINEERING 2021年 234卷 109292-109292页

作者： Chung, Woo Chul Kim, MooHyun Jin, Chungkuk Texas A&M Univ Dept Ocean Engn College Stn TX 77843 USA

In this paper, the methodology for the real-time tracing of riser profile and bending moment by multiple inclinometers along the riser is presented with the assumption that its top (by GPS;global positioning system) and bottom (anchoring) points are known. The riser is modelled as a series of slender-beam segments and at each time step, the riser nodal points are progressively traced. For riser x-y-z displacement and bending-moment tracing, quadratic and cubic interpolation functions for each line element between two neighboring bi-axial inclinometers are employed with respect to the global and generalized coordinate systems. Analytical solutions are derived for the instantaneous riser profile and bending moment at each line's mid-point. To validate the developed riser-monitoring algorithms, a FPSO (floating production storage offloading) system with SCR (steel catenary riser) or SLWR (steel lazy wave riser) was simulated for 1-yr and 50-yr collinear wind-wave-current storm conditions with 45-deg heading and the corresponding numerical sensor signals were inputted to the algorithms for reproducing the real-time riser profiles and bending stresses. Based on the comparisons between the algorithm-predicted and actual values, the developed method is validated. The methodology can be applied to any kinds and shapes of lines and cables too.

关键词： Riser profile tracing monitoring algorithm Inclinometer Bending moment Quadratic cubic polynomial

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An Approach for Dynamic Scheduling of Data Analysis algorithms 4

An Approach for Dynamic Scheduling of Data Analysis Algorith...

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4th IEEE International Conference on Big Data Analytics (ICBDA)

作者： Gui, Jun Zheng, Zeyu Gao, Yuan Qin, Zhaobo Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Network Control Syst Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

ISBN: (纸本)9781728112824

Dynamic scheduling of a set of algorithms is a key problem for data analysis platform. In this paper, we propose an approach to efficiently execute and monitor algorithms. Our approach classifies all algorithms into timing tasks, real-time tasks and equal interval times tasks and configures them separately. An intelligent strategy performs configuration checking for algorithms before scheduling them dynamically. The execution of each algorithm is monitored and controlled according to configuration and feedbacked operating information. Based on this approach, we develop an intelligent data analysis platform with more than 100 algorithms. By stable running for months, our approach is proved to be accurate and effective, and can be applied in many platforms.

关键词： data analysis Scheduling Strategy monitoring algorithm

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Distributed binary majority voting via exponential distribution

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IET SIGNAL PROCESSING 2016年第5期10卷 532-542页

作者： Salehkaleybar, Saber Golestani, Sayyed Jamaloddin Sharif Univ Technol Dept Elect Engn Tehran Iran

In the binary majority voting problem, each node initially chooses between two alternative choices. The goal is to design a distributed algorithm that informs nodes which choice is in majority. In this study, the authors formulate this problem as a hypothesis testing problem and propose fixed-size and sequential solutions using classical and Bayesian approaches. In the sequential version, the proposed mechanism enables nodes to test which choice is in majority, successively in time. Hence, termination of the algorithm is embedded within it, contrary to the existing approaches which require a monitoring algorithm to indicate the termination. This property makes the algorithm more efficient in terms of message complexity. Furthermore, the authors show that the proposed solution is resilient to Byzantine attacks if network connectivity is F + 1 in the presence of F adversarial nodes. Thus, the proposed algorithm is more robust compared with the previous works which are vulnerable to the existence of adversarial nodes.

关键词： exponential distribution signal processing distributed binary majority voting exponential distribution binary majority voting problem distributed algorithm hypothesis testing problem Bayesian approaches monitoring algorithm message complexity Byzantine attacks network connectivity adversarial nodes

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