Transformer Condition Monitoring using Fiber Optic Sensors: A Review

Review for research in a specific field plays an important role to find out the future scope of research work in that direction. This paper gives an extensive review about the application of optical sensors for condition monitoring of transformers. Monitoring of different parameters of transformer oil using optical sensors as a part of transformer condition monitoring has been discussed. In addition, classification and sensing principle of different optical sensors is also described. This paper also attempts to present few methodologies adopting for condition monitoring of power transformer.Keywords:Transformer, Optical fiber, Condition monitorin

Investigation of Wireless Sensor Deployed on a Rotating Shaft and Its Potential for Machinery Condition Monitoring

Rotating shafts are the most critical component of rotating machines such as motors, pumps, engines and turbines. Due to their heavy duties, defects are more like developed during operation. There are many techniques used to monitor shaft defects by analysing the vibration of the shaft as well as the instantaneous angular speed (IAS) of the shaft. The signal are measured either using non-contact techniques such as laser-based measurement or indirect measurement such as the vibration on bearing housings. The advancement in low cost and low power Micro Electro Mechanical Systems (MEMS) make it possible to develop an integrated wireless sensor which can be mounted on the shaft. This can make the fault diagnosis of rotating shafts more effective because the sensor can be mounted on the shaft directly. This paper presented a novel integrated wireless accelerometer for rotational parameter measurement. Its performance is benchmarked with that from a shaft encoder. Experimental results show that the wireless acceleration signal has less noise and hence it is more possible for small fault detection. Keywords: Wireless sensor, Rotating shaft, Instantaneous angular speed, Condition monitorin

Other Compressors: Reciprocating, Screw (Wet and Dry), Integrally Geared, and Turbo-Expander

Discussion GroupSuggested Topics: • Offshore and marine applications • Cryogenic considerations • Industry standards – API 617, 618, API 688, API 670, etc. • Bearings, seals, and couplings • Capacity Control – Speed, IGV, DGV, recycle, unloaders (all types) • Modern wear components – design, reliability and failures • Maintenance strategy / Best Practices • Process gas quality and conditioning • Pulsation, vibration and torsional issues • Packaging / Size and Weight Considerations / Installation Type • Field Testing and commissioning • Advanced Condition monitorin

Improving road asset condition monitoring

Road networks often carry more than 80% of a country’s total passenger-km and over 50% of its freight ton-km according to the World Bank. Efficient maintenance of road networks is highly important. Road asset management, which is essential for maintenance programs, consist of monitoring, assessing and decision making necessary for maintenance, repair and/or replacement. This process is highly dependent on adequate and timely pavement condition data. Current practice for collecting and analysing such data is 99% manual. To optimize this process, research has been performed towards automation. Several methods to automatically detect road assets and pavement conditions are proposed. In this paper, we present an analysis of the current state of practice of road asset monitoring, a discussion of the limitations, and a qualitative evaluation of the proposed automation methods found in the literature. The objective of this paper is to understand the issues associated with current processes, and assess the available tools to address these problems. The current state of practice is categorized into: 1) type of data collected; 2) type of asset covered and 3) amount of information provided. The categories are evaluated in terms of a) accuracy; b) applicability (efficiency); c) cost; and d) overall improvement to current practice. Despite the methods available, the outcome of the study indicates that current condition monitoring lacks efficiency, and none provide a holistic solution to the problem of road asset condition monitorin

Intelligent Tool Condition Monitoring In High-Speed Turning Of Titanium Ti-6Al-4V Alloy

Intelligent Tool Condition Monitoring (TCM) is an essential requirement in the drive towards automated machining operations. In this paper, a Multi-Layered Perceptron (MLP) neural net-work model has been developed for on-line condition monitoring of tool wear in high-speed turning of Titanium-based alloy (Ti-6Al-4V). Machining trials were conducted for typical rough and finish turning operations with cutting speed (90 – 120 m/min), feed rate (0.15 – 0.2 mm/rev), and depth of cut (0.5 -2.0 mm) using uncoated cemented carbide (K10 grade) inserts with Inter-national Standard Organization (ISO) designation “CNMG 120412”. The tool maximum flank wear (VBmax), cutting forces (feed force, Fx, and tangential force, Fz), and spindle motor power were measured during each machining operation. The cutting parameters (cutting speed, feed rate, and depth of cut), and cutting force and spindle power were used in isolation or in combi-nation as input dataset in training the neural network to predict wear land on cutting tool at different stages of wear propagation (light, medium and heavy). The neural network model was designed using Matlab® neural toolbox. Accuracy of model for the prediction of tool wear at dif-ferent wear stages were evaluated based on the Percentage Error (PE) for both roughing and finishing operations. Results showed that, the heavy wear stage (PE = ±5%) was predicted more accurately compared to the light (PE = +5 to -10%) and medium (PE = +25 to -30%) wear stages. The combination of the force, power signals and cutting parameters improved perform-ance of the model.Keywords: Artificial neutral network, Turning, Ti-6Al-4V alloy; Tool wear, Condition monitorin

Computer aided detection of defects in FRP bridge decks using infrared thermography

The objective of this research is to develop a turn-key system that is able to interface with the FLIR ThermaCAM S60 infrared camera and automatically capture and analyze defects in infrared images of FRP bridge decks. Infrared thermography is one of the nondestructive evaluation (NDE) techniques that are being used to locate defects (debonds and delaminations) in bridge components. It is a rapid data collection and interpretation technique having high sensitivity and reliability. Analysis of infrared images by human interpretation is dependent on the users knowledge and hence introduces ambiguity in the defect detection process.;This thesis investigates the use of an automated defect detection system to locate defects in infrared images of FRP bridge decks to eliminate/reduce human intervention. Air-filled and water-filled debonds were inserted between the wearing surface and the underlying FRP deck. Also, simulated subsurface delaminations (of various sizes and thickness) were created at the flange-to-flange junction between two FRP deck modules. (Abstract shortened by UMI.)

Fault detection using transfer function techniques

SIGLEAvailable from British Library Document Supply Centre- DSC:D75688/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Performance deterioration based on in-service engine data: JT9D jet engine diagnostics program

Results of analyses of engine performance deterioration trends and levels with respect to service usage are presented. Thirty-two JT9D-7A engines were selected for this purpose. The selection of this engine fleet provided the opportunity of obtaining engine performance data starting before the first flight through initial service such that the trend and levels of engine deterioration related to both short and long term deterioration could be more carefully defined. The performance data collected and analyzed included in-flight, on wing (ground), and test stand prerepair and postrepair performance calibrations with expanded instrumentation where feasible. The results of the analyses of these data were used to: (1) close gaps in previously obtained historical data as well as augment the historical data with more carefully obtained data; (2) refine preliminary models of performance deterioration with respect to usage; (3) establish an understanding of the relationships between ground and altitude performance deterioration trends; (4) refine preliminary recommendations concerning means to reduce and control deterioration; and (5) identify areas where additional effort is required to develop an understanding of complex deterioration issues