Test rig description

The test rig comprises of a two-stage, single-acting Broom Wade TS9 reciprocating compressor, which has two cylinders, designed to deliver compressed air between 0.55MPa and 0.8MPa to a horizontal air receiver tank with a maximum working pressure of about 1.38MPa. As shown in Figure 7, the driving motor was a three phase, squirrel cage, air cooled, type KX-C184, 2.5kW induction motor. It was mounted on the top of the receiver and transfers its power to the compressor through a pulley belt system. The transmission ratio is 3.2, which results in a crank shaft speed of 440 rpm when the motor runs at its rated speed of 1420 rpm. The air in the first cylinder is compressed and passed to the higher-pressure cylinder via an air-cooled intercooler.

Instrumentation

The basic set up can measure vibration, current, pressure, speed, temperature simultaneously at different discharge pressures ranged from 0.2 to 1.2MPa in a step of 0.1MPa. In addition, thermal IR, acoustic emission, and airborne acoustic analysis can also be performed with a separate data acquisition system triggered by the present data acquisition system.

Typical fault simulation

The basic set up can measure vibration, current, pressure, speed, temperature simultaneously at different discharge pressures ranged from 0.2 to 1.2MPa in a step of 0.1MPa. In addition, thermal IR, acoustic emission, and airborne acoustic analysis can also be performed with a separate data acquisition system triggered by the present data acquisition system.

Key publications

• [1] Elhaj, M., Gu, F., Ball, A.D., Albarbar, A., Al-Qattan, M. and Naid, A., 2008. Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring. Mechanical Systems and Signal Processing, 22(2), pp.374-389.
• [2] Feng, G., Hu, N., Mones, Z., Gu, F. and Ball, A.D., 2016. An investigation of the orthogonal outputs from an on-rotor MEMS accelerometer for reciprocating compressor condition monitoring. Mechanical Systems and Signal Processing, 76, pp.228-241.
• [3] Haba, U., Brethee, K., Alabied, S., Mondal, D., Gu, F. and Ball, A., 2018. Modelling and Simulation of a Two Stage Reciprocating Compressor for Condition Monitoring Based on Motor Current Signature Analysis. Condition Monitoring and Diagnostic Engineering Management, 2.
• [4] Mondal, D., Zhen, D., Gu, F. and Ball, A.D., 2020. Fault Diagnosis of Reciprocating Compressor Using Empirical Mode Decomposition-Based Teager Energy Spectrum of Airborne Acoustic Signal. In Advances in Asset Management and Condition Monitoring (pp. 939-952). Springer, Cham.
• [5] Haba, U., Feng, G., Shaeboub, A., Peng, X., Gu, F. and Ball, A., 2016. Detection and Diagnosis of Compound Faults in a Reciprocating Compressor based on Motor Current Signatures.
• [6] Ahmed, M., Gu, F. and Ball, A., 2011, July. Feature selection and fault classification of reciprocating compressors using a genetic algorithm and a probabilistic neural network. In Journal of Physics-Conference Series (Vol. 305, No. 1, p. 012112).
• [7] Naid, A., Gu, F.S., Shao, Y.M., Al-Arbi, S. and Ball, A., 2009. Bispectrum analysis of motor current signals for fault diagnosis of reciprocating compressors. In Key Engineering Materials (Vol. 413, pp. 505-511). Trans Tech Publications Ltd.
• [8] Al-Qattan, M., Al-Juwayhel, F., Ball, A., Elhaj, M. and Gu, F., 2009. Instantaneous angular speed and power for the diagnosis of single-stage, double-acting reciprocating compressor. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 223(1), pp.95-114.
• [9] Liang, B., Gu, F. and Ball, A., 1996. A preliminary investigation of valve fault diagnosis in reciprocating compressors. Maintenance & Asset Management Journal, 11(2), pp.2-8.
• [10] Mondal, D., Gu, F. and Ball, A., 2019. Application of Minimum Entropy Deconvolution in Diagnosis of Reciprocating Compressor Faults Based on Airborne Acoustic Analysis.
• [11] Ahmed, M., Gu, F. and Ball, A.D., 2012, May. Fault detection of reciprocating compressors using a model from principles component analysis of vibrations. In Journal of Physics: Conference Series (Vol. 364, No. 1, p. 012133). IOP Publishing.
• [12] Mondal, D., Sun, X., Gu, F. and Ball, A., 2018. A Study of Diagnosing Reciprocating Compressor Faults using EMD-entropy of the Airborne Acoustic Signals. ShieldCrest Publishing.
• [13] Muo, U.E., Madamedon, M., Ball, A.D. and Gu, F., 2017, September. Wavelet packet analysis and empirical mode decomposition for the fault diagnosis of reciprocating compressors. In 2017 23rd International Conference on Automation and Computing (ICAC) (pp. 1-6). IEEE.
• [14] Ahmed, M., Baqqar, M., Gu, F. and Ball, A.D., 2012, September. Fault detection and diagnosis using Principal Component Analysis of vibration data from a reciprocating compressor. In Proceedings of 2012 UKACC international conference on control (pp. 461-466). IEEE.
• [15] Ahmed, M., Abdusslam, S., Baqqar, M., Gu, F. and Ball, A.D., 2011, September. Fault classification of reciprocating compressor based on neural networks and support vector machines. In The 17th International Conference on Automation and Computing (pp. 213-218). IEEE.
• [16] Mones, Z., Feng, G., Ogbulaor, U.E., Wang, T., Gu, F. and Ball, A., 2016, October. Performance evaluation of wireless MEMS accelerometer for reciprocating compressor condition monitoring. In Power Engineering: Proceedings of the International Conference on Power Transmissions 2016 (ICPT 2016), Chongqing, PR China, 27–30 October 2016 (No. 2016, pp. 893-900). Taylor & Francis.
• [17] Mondal, D., Haba, U., Gu, F. and Ball, A., 2019, September. Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum. In 2019 25th International Conference on Automation and Computing (ICAC) (pp. 1-5). IEEE.