A Novel Approach for Extracting Sensor Information From Different Industrial Networks for Internet Based Condition Monitoring Applications

Volume 2 ◽  
2004 ◽  
Author(s):  
Bilal Ashraf ◽  
Farbod Zorriassatine ◽  
R. M. Parkin ◽  
Joanne Coy
Keyword(s):  
Condition Monitoring ◽  
Area Network ◽  
Standard Format ◽  
Industrial Networks ◽  
Novel Approach ◽  
Network Controller ◽  
Daunting Task ◽  
Extensible Markup ◽  
Sensor Information ◽  
Single Standard

Automated Condition Monitoring (ACM) has become a necessity for complex modern day systems. The advent and ever increasing popularity of Internet has given a new dimension to ACM. Many Internet Based Condition Monitoring (IBCM) solutions have since been implemented. There are many types of Industrial Networks that are used in the industry to implement ACM. The protocols and information sent through these networks are very different from one another. Sharing information between industrial networks and presenting it for consolidated monitoring can be a daunting task. This paper describes a novel way for extracting sensor information from different industrial networks into a single standard format using Extensible Markup Language (XML). Implementation of the solution with an Industrial Network, Controller Area Network (CAN), is also shown. The results demonstrate that by using this approach communication between automated systems and mechatronic devices will become more integrated, more efficient and less complex.

scholarly journals Smarter maintenance through internet-based condition monitoring with indirect sensing, novelty detection, and XML

2005 ◽  
Vol 219 (4) ◽  
pp. 283-293
Author(s):  
F Zorriassatine ◽  
B Ashraf ◽  
L Notini ◽  
R M Parkin ◽  
M R Jackson ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Novelty Detection ◽  
Low Cost ◽  
Cost Effective ◽  
Integrated Approach ◽  
Detection Accuracy ◽  
Standard Format ◽  
The Status ◽  
Single Standard ◽  
Critical Components

In engineering, combining a number of solutions and technologies can result in more effective systems than using only one approach on its own. In particular, it has been shown that in condition monitoring (CM), smarter maintenance systems may be obtained by integrating various sensors together. This paper extends this idea by integrating various non-homogeneous technologies horizontally. The proposed system is an internet-based condition monitoring (e-CM) prototype that can identify abnormal tension in moving belts. It is shown that by applying a classification technique, known as novelty detection, it is possible to decide the status of belt tension by processing the belt vibration signals from an optical sensor (i.e. an indirect sensing approach). A novel method for industrial network communication using XML to create a single standard format for sensor information is also used to link the sensor to the process controller via the internet using the flexible CAN bus technology; this is used together with low-cost microcontrollers with a built-in ethernet link for data acquisition and transmission. The resulting integrated approach is more efficient because: (a) it can reduce waste by minimizing process interruptions caused by direct belt inspection methods while obtaining high detection accuracy (99.67 per cent) and (b) it can provide on-line remote CM that is cost-effective, simple, standardized, and scalable across a wide area and for a relatively large number of sensors. This improvement is especially important when applied to bottleneck processes and critical components.

LASRE: A Novel Approach to Large area Accelerated Segmentation for Reverse Engineering on SEM images

2020 ◽  
Author(s):  
Ronald Wilson ◽  
Domenic Forte ◽  
Navid Asadizanjani ◽  
Damon L. Woodard
Keyword(s):  
Integrated Circuits ◽  
Reverse Engineering ◽  
Imaging Modality ◽  
Primary Source ◽  
Time Frame ◽  
Large Area ◽  
Sem Images ◽  
Model Free ◽  
Novel Approach ◽  
Daunting Task

Abstract In the hardware assurance community, Reverse Engineering (RE) is considered a key tool and asset in ensuring the security and reliability of Integrated Circuits (IC). However, with the introduction of advanced node technologies, the application of RE to ICs is turning into a daunting task. This is amplified by the challenges introduced by the imaging modalities such as the Scanning Electron Microscope (SEM) used in acquiring images of ICs. One such challenge is the lack of understanding of the influence of noise in the imaging modality along with its detrimental effect on the quality of images and the overall time frame required for imaging the IC. In this paper, we characterize some aspects of the noise in the image along with its primary source. Furthermore, we use this understanding to propose a novel texture-based segmentation algorithm for SEM images called LASRE. The proposed approach is unsupervised, model-free, robust to the presence of noise and can be applied to all layers of the IC with consistent results. Finally, the results from a comparison study is reported, and the issues associated with the approach are discussed in detail. The approach consistently achieved over 86% accuracy in segmenting various layers in the IC.

Chipless RFID Sensor for High Voltage Condition Monitoring

2012 ◽  
pp. 304-333
Author(s):  
Emran Md Amin ◽  
Nemai Chandra Karmakar
Keyword(s):  
Condition Monitoring ◽  
High Voltage ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Base Station ◽  
Novel Approach ◽  
Frequency Identification ◽  
On Line ◽  
Passive Rfid ◽  
Passive Circuit

A novel approach for non-invasive radiometric Partial Discharge (PD) detection and localization of faulty power apparatuses in switchyards using Chipless Radio Frequency Identification (RFID) based sensor is presented. The sensor integrates temperature sensing together with PD detection to assist on-line automated condition monitoring of high voltage equipment. The sensor is a multi-resonator based passive circuit with two antennas for reception of PD signal from the source and transmission of the captured PD to the base station. The sensor captures PD signal, processes it with designated spectral signatures as identification data bits, incorporates temperature information, and retransmits the data with PD signals to the base station. Analyzing the PD signal in the base station, both the PD levels and temperature of a particular faulty source can be retrieved. The prototype sensor was designed, fabricated, and tested for performance analysis. Results verify that the sensor is capable of identifying different sources at the events of PD. The proposed low cost passive RFID based PD sensor has a major advantage over existing condition monitoring techniques due to its scalability to large substations for mass deployment.

scholarly journals A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4493
Author(s):  
Rui Silva ◽  
António Araújo
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Recurrent Neural Networks ◽  
Cutting Process ◽  
Time Dependent ◽  
Industrial Processes ◽  
Fundamental Part ◽  
Novel Approach ◽  
Noisy Signals ◽  
Proper Condition

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.

Integrating PDF interface into Java application

2014 ◽  
Vol 32 (3) ◽  
pp. 495-508 ◽  
Author(s):  
Quan Lu ◽  
Gao Liu ◽  
Jing Chen
Keyword(s):  
Digital Library ◽  
Complex Structure ◽  
Portable Document Format ◽  
Content Type ◽  
Service Specification ◽  
Java Application ◽  
Novel Approach ◽  
Extensible Markup ◽  
Further Development ◽  
Practical Implications

Purpose – The purpose of this paper is to propose a novel approach to integrate portable document format (PDF) interface into Java-based digital library application. It bridges the gap between conducting content operation and viewing on PDF document asynchronously. Design/methodology/approach – In this paper, the authors first review some related research and discuss PDF and its drawbacks. Next, the authors propose the design steps and implementation of three modes of displaying PDF document: PDF display, image display and extensible markup language (XML) display. A comparison of these three modes has been carried out. Findings – The authors find that the PDF display is able to completely present the original PDF document contents and thus obviously superior to the other two displays. In addition, the format specification of PDF-based e-book does not perform well; lack of standardization and complex structure is exposed to the publication. Practical implications – The proposed approach makes viewing the PDF documents more convenient and effective, and can be used to retrieve and visualize the PDF documents and to support the personalized function customization of PDF in the digital library applications. Originality/value – This paper proposes a novel approach to solve the problem between content operation and the view of PDF synchronously, providing users a new tool to retrieve and reuse the PDF documents. It contributes to improve the service specification and policy of viewing the PDF for digital library. Besides, the personalized interface and public index make further development and application more feasible.

A novel approach to machining condition monitoring of deep hole boring

2014 ◽  
Vol 77 ◽  
pp. 27-33 ◽  
Author(s):  
Wenrong Xiao ◽  
Yanyang Zi ◽  
Binqiang Chen ◽  
Bing Li ◽  
Zhengjia He
Keyword(s):  
Condition Monitoring ◽  
Deep Hole ◽  
Novel Approach

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

scholarly journals Indoor Localization within Multi-Story Buildings Using MAC and RSSI Fingerprint Vectors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2433 ◽  
Author(s):  
Litao Han ◽  
Li Jiang ◽  
Qiaoli Kong ◽  
Ji Wang ◽  
Aiguo Zhang ◽  
...  
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Media Access Control ◽  
Area Network ◽  
Media Access ◽  
Novel Approach ◽  
The Difference

For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings.

A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN

2021 ◽  
Vol 10 (2) ◽  
pp. 44-65
Author(s):  
Koushik Karmakar ◽  
Sohail Saif ◽  
Suparna Biswas ◽  
Sarmistha Neogy
Keyword(s):  
Condition Monitoring ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Health Condition ◽  
Sensor Data ◽  
Area Network ◽  
Cardiac Condition ◽  
Artificial Neural Network Ann ◽  
Sensor Detection

Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

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