Characterisation of major fault detection features and techniques for the condition-based monitoring of high-speed centrifugal blowers

This paper investigates and characterises the major fault detection signal features and techniques for the diagnostics of rotating element bearings and air leakage faults in high-speed centrifugal blowers. The investigation is based on time domain and frequency domain analysis, as well as on process information, vibration, and acoustic emission fault detection techniques. The results showed that the data analysis method applied in this study is effective, as it yielded a detection accuracy of 100%. A lookup table was compiled to provide an integrated solution for the developer of Condition-Based Monitoring (CBM) applications of centrifugal blowers. The major contribution of this paper is the integration and characterisation of the major fault detection features and techniques

Profitability, reliability and condition based monitoring of LNG floating platforms: a review

The efficiency and profitability of Floating, Production, Storage and Offloading platform (FPSO) terminals depends on various factors such as LNG liquefaction process type, system reliability and maintenance approach. This review is organized along the following research questions: (i) what are the economic benefit of FPSO and how does the liquefaction process type affect its profitability profile?, (ii) how to improve the reliability of the liquefaction system as key section? and finally (iii) what are the major CBM techniques applied on FPSO. The paper concluded the literature and identified the research shortcomings in order to improve profitability, efficiency and availability of FPSOs

Performance comparison of different flow arrangements of 4-fluid internally-cooled liquid desiccant dehumidifiers

In this study, the performance of 10 different flow arrangements of 4-fluid internally-cooled liquid desiccant dehumidifiers were compared. The four fluids are supply air, exhaust air, liquid desiccant, and water. The comparison was performed using a two-dimensional heat and mass transfer model of the dehumidifier that was solved numerically. The model’s predictions of supply air outlet humidity ratio matched experimental measurements within 6.7%. The two-dimensional variation of the air temperature and humidity ratio in the supply channel showed the importance of using a two-dimensional heat and mass transfer model when at least one of the fluids is in cross-flow with the other fluids. Moreover, a sensitivity analysis was performed to evaluate the effect of nine input parameters (supply air temperature and humidity ratio, exhaust air temperature and humidity ratio, liquid desiccant temperature, concentration, and flow rate, supply air mass flow rate, and exhaust to supply air mass flow rate ratio) on the performance of the dehumidifiers. The results showed that the best performance, in terms of the supply air humidity ratio and enthalpy decrease, was obtained when the supply air was in counter-flow with the exhaust air, liquid desiccant, and water. While the poorest performance was obtained when the supply air was in parallel-flow with the exhaust air and in counter-flow with the liquid desiccant and water. The approximate difference between the best and poorest performing flow arrangements in terms of the decrease in supply air humidity ratio and enthalpy is 4.3% and 10.5%, respectively. The results of the sensitivity analysis showed that for the 10 flow arrangements, the liquid desiccant inlet temperature, and flow rate have the least effects on the performance of the dehumidifier.Other Information Published in: Journal of Thermal Analysis and Calorimetry License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s10973-022-11283-x</p

Performance comparison between FFT-based segmentation, feature selection and fault identification algorithm and neural network for the condition monitoring of centrifugal equipment

This paper compares and evaluates the performance of two major feature selection and fault identification methods utilized for the Condition Monitoring (CM) of centrifugal equipment, namely FFT-based Segmentation, Feature Selection, and Fault Identification (FS2FI) algorithm and Neural Network (NN). Mutli-Layer Perceptron is the most commonly used NN model for fault pattern recognition. Feature-selection and Trending play an important role in pattern recognition, and hence, affect the performance of CM systems. The technical and developmental challenges of both methods were investigated experimentally on a Paxton industrial centrifugal air blower system with a rotational speed of 15,650 RPM‟s. Five different machine conditions were experimentally emulated in the laboratory. A low training-to-testing ratio of 50% was utilized to evaluate the performance of both methods. In order to maximize fault identification accuracy and minimize computing time and cost, a near-optimal NN configuration was identified. The results showed that both techniques operated with a fault identification accuracy of 100%. However, the FS2FI algorithm showed a number of advantages over NN. These advantages include the ease of implementation and a reduction of cost and time in development and computing, as it processed the data from the first trial in less than 6.2% of the time taken by the neural network