A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems

2021 ◽  
Author(s):  
Zhangyue Shi ◽  
Chenang Liu ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Yang Chen
Keyword(s):  
Information Technologies ◽  
Manufacturing Systems ◽  
Rapid Development ◽  
Data Access ◽  
Sensor Data ◽  
False Alarms ◽  
Monitoring And Control ◽  
Stream Data ◽  
Physical Security ◽  
And Control

Abstract With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

Theft Preventive Measures for Interconnected Personal Computer Devices as Proactive Physical Security of Data

2021 ◽  
pp. 337-352
Author(s):  
Ekaterina Pshehotskaya ◽  
Oleg Mikhalsky
Keyword(s):  
User Behavior ◽  
Personal Data ◽  
Data Access ◽  
Security And Privacy ◽  
Portable Devices ◽  
Physical Security ◽  
Security Risks ◽  
Portable Computer ◽  
Relative Risks ◽  
And Control

This article is concerned with the arising problems and implications of physical security and privacy of personal and control data on portable computer devices, especially smartphones. The authors consider various classifications of portable computer devices, isolate smartphones as a most common device, and study types of user behavior regarding the involved security risks of unauthorized access to the data stored both locally and remotely with accent of physical data access via device theft. Based on provided categorization the researchers discuss the factors and criteria suitable to generalize user patterns and evaluate the corresponding vulnerability level against specified statistics. The considered statistical criteria can be formulated as a mathematical model of relative risks and implemented as a service or an application to be used for improving user awareness on current threats to his personal data and respective interconnected personal portable devices.

scholarly journals False Alarms Analysis of Wind Turbine Bearing System

2020 ◽  
Vol 12 (19) ◽  
pp. 7867 ◽  
Author(s):  
Ana María Peco Chacón ◽  
Isaac Segovia Ramírez ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Speed ◽  
Condition Monitoring ◽  
Wind Farms ◽  
Data Partitioning ◽  
False Alarms ◽  
Monitoring And Control ◽  
Condition Monitoring Systems ◽  
Mining Centers ◽  
Critical Components ◽  
And Control

Wind turbines are complex systems that use advanced condition monitoring systems for analyzing their health status. The gearbox is one of the most critical components due to its elevated downtime and failure rate. Supervisory Control and Data Acquisition systems are employed in wind farms for condition monitoring and control in real time. The volume and variety of the data require novel and robust techniques for data analysis. The main novelty of this work is the development of a new modelling of the temperature curve of the gearbox bearing versus wind speed to detect false alarms. An approach based on data partitioning and data mining centers is employed. The wind speed range is divided into intervals to increase the accuracy of the model, where the centers are considered representative samples in the modelling. A method based on the alarm detection is developed and studied together with the alarms report provided by a real case study. The results obtained allow the identification of critical alarm periods outside the confidence interval. It is validated that the study of alarm identification, pre-filtered data, state variable, and output power contribute to the detection of the false alarms.

A Bio-Inspired Framework for a Self-Healing Assembly System

2011 ◽  
Author(s):  
Lee J. Wells ◽  
Jaime A. Camelio ◽  
Giovannina Zapata
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
System Level ◽  
Self Healing ◽  
Monitoring And Control ◽  
Statistical Process ◽  
Control Approach ◽  
Process Monitoring And Control ◽  
Manufacturing Assembly ◽  
And Control

Statistical process monitoring and control has been popularized throughout the manufacturing industry as well as various other industries interested in improving product quality and reducing costs. Advances in this field have focused primarily on more efficient ways for diagnosing faults, reducing variation, developing robust design techniques, and increasing sensor capabilities. System level advances are largely dependent on the introduction of new techniques in the listed areas. A unique system level quality control approach is introduced in this paper as a means to integrate rapidly advancing computing technology and analysis methods in manufacturing systems. Inspired by biological systems, the developed framework utilizes immunological principles as a means of developing self-healing algorithms and techniques for manufacturing assembly systems. The principles and techniques attained through this bio-mimicking approach will be used for autonomous monitoring, detection, diagnosis, prognosis, and control of station and system level faults, contrary to traditional systems that largely rely on final product measurements and expert analysis to eliminate process faults.

scholarly journals Building an HMI and Demo Application of WSN-based Industrial Control Systems

2011 ◽  
Vol 7 (2) ◽  
pp. 107-111
Author(s):  
Ali Abed ◽  
AbdulAdhem Ali ◽  
Nauman Aslam
Keyword(s):  
Control Systems ◽  
Low Cost ◽  
Data Access ◽  
Industrial Applications ◽  
Base Station ◽  
Temperature Monitoring ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Monitoring And Control ◽  
And Control

In this paper we present the details of methodology pursued in implementation of an HMI and Demo Temperature Monitoring application for wireless sensor-based distributed control systems. The application of WSN for a temperature monitoring and control is composed of a number of sensor nodes (motes) with a networking capability that can be deployed for monitoring and control purposes. The temperature is measured in the real time by the sensor boards that sample and send the data to the monitoring computer through a base station or gateway. This paper proposes how such monitoring system can be setup emphasizing on the aspects of low cost, energy-efficient, easy ad-hoc installation and easy handling and maintenance. This paper focuses on the overall potential of wireless sensor nodes and networking in industrial applications. A specific case study is given for the measurement of temperature (with thermistor or thermocouple), humidity, light and the health of the WSN. The focus was not on these four types of measurements and analysis but rather on the design of a communication protocol and building of an HMI software for monitoring. So, a set of system design requirements are developed that covered the use of the wireless platforms, the design of sensor network, the capabilities for remote data access and management, the connection between the WSN and an HMI software designed with MATLAB.

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

Performance Test Results of a Precision Data Acquisition and Control Platform for 200°C+ High Temperature Environments

2018 ◽  
Vol 2018 (HiTEC) ◽  
pp. 000103-000111
Author(s):  
Jeff Watson ◽  
Maithil Pachchigar ◽  
Ross Bannatyne ◽  
Clay Merritt ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
High Temperature ◽  
Low Power ◽  
Data Acquisition ◽  
Performance Test ◽  
Rapid Development ◽  
Sensor Data ◽  
Harsh Environment ◽  
Test Results ◽  
Precision Data ◽  
And Control

Abstract In recent years there has been an increasing selection of commercially available electronic components specified for very high temperature (200C+) operation, driven by the needs of harsh-environment applications such as oil and gas exploration/production, aerospace, heavy industrial, and automotive. However, there remains a significant technical challenge to integrate these components into reliable, high performance systems. We previously presented a complete reference design of a precision multichannel sensor data acquisition and control system rated to 200C, including characterized hardware, firmware, and software. The design is based around low power 16 bit SAR ADCs and an ARM® Cortex®-M0 processor and is optimized for high resolution and high throughput measurements while maintaining low power and a small footprint. In this paper we present the test results of this system over temperature. The reference platform is available off the shelf, including hardware design files, processor firmware source code, and PC software for data logging and display, providing engineers a rapid development tool for prototyping and a faster path to production for complex harsh-environment applications.

scholarly journals Sensor Data Analysis On Monitoring And Control System Of Temperature And Humidity Based On Android In Soybean Seed Storage Room Using Nodemcu

JAICT ◽  
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sindung HW Sasono
Keyword(s):  
Control System ◽  
Data Analysis ◽  
Soybean Seed ◽  
Seed Storage ◽  
Sensor Data ◽  
Monitoring And Control ◽  
Storage Room ◽  
Temperature State ◽  
Temperature And Humidity ◽  
And Control

soybean seeds may be damaged during storage time. Temperature and humidity of soybean seed storage room, is one of the external factors of damage to the seed. The system consists of monitoring and control temperature and humidity in some rooms used as soybean seed storage room samples. This study discusses and perform sensor data analysis using several types of temperature and humidity sensors based on Internet of Things. Sensor nodes generate data and processed by a microcontroller NodeMCU ESP8266, and the results data is then transmitted by the Internet network using MQTT broker and stored in the database. Results data is then analyzed to monitor the condition of soybean seed storage room. SHT30 sensor has the most excellent temperature accuracy of 98, 21%. DHT22 sensor has the most excellent moisture accuracy of 95.74%. Data sending to the database has a good level of dataloss category for node 1 is 3.39%, 4.33% for node 2, and 3.22% for node 3. Air conditioner control system using Android can keep the room temperature state in the range of 18-23oC and humidity of 40-60% with an air conditioning remote control setting at 20oC on an area of 36 m2.

Sign in / Sign up

Export Citation Format

Share Document