A numerical simulation of a two-stage reciprocating compressor has replicated the operations of the compressor under various conditions for the development of diagnostic features for predictive condition monitoring. The simulation involves the development of a mathematical model of five different physical processes: speed–torque characteristics of an induction motor, cylinder pressure variation, crankshaft rotational motion, flow characteristics through valves and vibration of the valve plates. Modelling both valve leakage and valve spring deterioration has also been achieved. The simulation was implemented in a MATLAB environment for an efficient numerical solution and ease of result presentation. For normal operating conditions, the simulated results are in good agreement with the test results for cylinder pressure waveforms and crankshaft instantaneous angular speed (IAS). It has been found that both the IAS 

The induction motor is the most common driver in industry and has been previously proposed as a means of inferring the condition of an entire equipment train, predominantly through the measurement and processing of power supply parameters. This has obvious advantages in terms of being non-intrusive or remote, less costly to apply and improved safety. This paper describes the use of the induction motor current to identify and quantify a number of common faults seeded on a two-stage reciprocating compressor. An analysis of the compressor working cycle leads to current signal the components that are sensitive to the common faults seeded to compressor system, and second- and third-order signal processing tools are used to analyse the current signals. It is shown that the developed diagnostic features: the bispectral peak value from the amplitude modulation bispectrum and the kurtosis from the current gives rise to reliable fault classification results. The low feature values can differentiate the belt looseness from other fault cases and valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis stator current data for the diagnosis of motor drive faults

Abstract. In many cases, it is impractical to measure the vibrations directly at or close to their source. It is a common practice to measure the vibration at a location far from the source for condition monitoring purposes. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. The suppression of the distortions is a key issue for the remote measurements based condition monitoring. In this paper, the influences of transducer locations are investigated on a typical gearbox transmission system for the detection of the faults induced to the gearbox. Several signal processing techniques’ analysis results show that the attenuation and interference cause high influences on the gear transmission signals. However, time synchronous average (TSA) is very effective to detect the local faults induced to the gear system.

Abstract

A numerical simulation of a two-stage reciprocating compressor has replicated the operations of the compressor under various conditions for the development of diagnostic features for predictive condition monitoring. The simulation involves the development of a mathematical model of five different physical processes: speed–torque characteristics of an induction motor, cylinder pressure variation, crankshaft rotational motion, flow characteristics through valves and vibration of the valve plates. Modelling both valve leakage and valve spring deterioration has also been achieved. The simulation was implemented in a MATLAB environment for an efficient numerical solution and ease of result presentation. For normal operating conditions, the simulated results are in good agreement with the test results for cylinder pressure waveforms and crankshaft instantaneous angular speed (IAS). It has been found that both the IAS fluctuation and pressure waveform are sensitive detection features for compressor faults such as valve leakage and valve spring deterioration. However, IAS is preferred because of its non-intrusive measurement nature. Further studies using the model and experiments are being undertaken in order to develop fault detection features for compressor driving motors and transmission systems.