Design and Performance of the BCM1F Front End ASIC for the Beam Condition Monitoring System at the CMS Experiment

2016 ◽  
Vol 63 (4) ◽  
pp. 2300-2308 ◽  
Author(s):  
Dominik Przyborowski ◽  
Jan Kaplon ◽  
Piotr Rymaszewski
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Cms Experiment ◽  
Front End ◽  
Condition Monitoring System ◽  
And Performance

Trends™: An Automatic Gas Turbine Diagnostic System

1975 ◽  
Author(s):  
J. R. Passalacqua
Keyword(s):  
Gas Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Diagnostic System ◽  
Data Systems ◽  
Standard Field ◽  
Military Aviation ◽  
Condition Monitoring System ◽  
And Performance ◽  
Engine Condition

This paper describes the development, operation and performance of an automatic engine condition monitoring system by Hamilton Standard Division of United Aircraft. This development is a direct outgrowth of Airborne Integrated Data Systems (AIDS), which have been developed for commercial and military aviation. Application of this technology to an installation at Hartford Electric Light Company’s South Meadow facility led to the development of the system currently being installed at several major utilities and marketed by Hamilton Standard. Field results of the HELCO testing are presented herein. As current installation information becomes available it will be made available to industry.

Marine Gas Turbine Condition Monitoring by Gas Path Electrostatic Detection Techniques

1991 ◽  
Author(s):  
R. A. Cartwright ◽  
C. Fisher
Keyword(s):  
Gas Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Joint Research ◽  
Detection Techniques ◽  
Debris Particles ◽  
On Line ◽  
Path Condition ◽  
Condition Monitoring System ◽  
And Performance

It was discovered in 1970 that certain gas turbine failures are preceded by an increase in electrostatic activity in the exhaust gases. Joint research by the Royal Aerospace Establishment and Stewart Hughes Limited demonstrated that this characteristic could be used to provide an on-line monitor of the precursors to these failures. An extension of the research applied the theory to the detection of foreign objects ingested into engine inlets. The characteristics and performance of both the Ingested Debris Monitoring System (IDMS) and Engine Distress Monitoring System (EDMS) were examined during a recent 2000 hours endurance trial of a Rolls-Royce Marine Spey gas turbine. The EDMS produced clear evidence of the minor combustor degradation that occurred steadily throughout the trial and also reflected the absence of other engine damage. IDMS data showed that few significant debris particles passed through the engine. Video endoscope and visual inspection confirmed these results. Debris seeding trials further explored the capability of the IDMS to identify the damaging nature of debris and to assess the EDMS signature of consequential engine damage. The paper concludes that electrostatic monitoring at engine inlet and exhaust can identify the ingestion of debris, consequential engine damage and the onset of unexpected distresses caused by blade rubs or combustor degradation. The technique shows potential to provide early warning of certain types of engine damage to Engineer Officers at sea and development into a rugged gas path condition monitoring system continues.

scholarly journals Proposed Smart Monitoring System for the Detection of Bee Swarming

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 87
Author(s):  
George Voudiotis ◽  
Sotirios Kontogiannis ◽  
Christos Pikridas 
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Learning Process ◽  
System Architecture ◽  
Image Acquisition ◽  
Condition Monitoring System ◽  
End Node ◽  
And Performance ◽  
Cloud Cores

This paper presents a bee-condition-monitoring system incorporated with a deep-learning process to detect bee swarming. This system includes easy-to-use image acquisition and various end node approaches for either on-site or cloud-based mechanisms. This system also incorporates a new smart CNN engine called Swarm-engine for detecting bees and the issue of notifications in cases of bee swarming conditions to the apiarists. First, this paper presents the authors’ proposed implementation system architecture and end node versions that put it to the test. Then, several pre-trained networks of the authors’ proposed CNN Swarm-engine were also validated to detect bee-clustering events that may lead to swarming. Finally, their accuracy and performance towards detection were evaluated using both cloud cores and embedded ARM devices on parts of the system’s different end-node implementations.

Online Real-Time Monitoring System Through Using Adaptive Angular-Velocity VKF Order Tracking

2012 ◽  
Author(s):  
Ting-Chi Yeh ◽  
Min-Chun Pan
Keyword(s):  
Angular Velocity ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Theoretical Derivation ◽  
Periodic Loading ◽  
Order Tracking ◽  
Computation Scheme ◽  
Condition Monitoring System ◽  
Operation Status

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

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