A sensor-less stroke detection technique for linear refrigeration compressors using artificial neural network. (June 2020)

• Record Type: Journal Article
• Authors: Jiang, Hanying; Liang, Kun; Li, Zhaohua; Zhu, Zhennan; Zhi, Xiaoqin; Qiu, Limin
• Is Part Of: International journal of refrigeration. Volume 114(2020)
• Page Start: 62
• Page End: 70
• Publication Date: 2020-06
• Subjects: Artificial neural network -- Harmonic analysis -- Linear compressor -- Stroke detection -- Sensor-less; Réseau neuronal artificiel -- Analyse harmonique -- Compresseur linéaire -- Détection des chocs -- Sans capteur; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2020.02.037
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: An ANN model based stroke prediction for linear compressors was introduced. The mean squared errors are improved with the increase of harmonic terms. Six harmonic terms as inputs demonstrates better percentage error distribution. The case with three harmonic terms as inputs is recommended. The ANN based stroke prediction can be adopted for linear compressor stroke detection. Abstract: Linear compressors are very attractive for domestic refrigeration due to elimination of crank mechanism, high efficiency and compactness compared with conventional compressors. The significance of stroke control in a linear compressor not only lies in avoiding the piston collision into the cylinder head but also enabling cooling capacity modulation. Predicting piston stroke without a displacement sensor reduces the cost and facilitates the stroke control especially in miniature linear compressor where there is very limited space for installing sensors. This paper reports an artificial neural network (ANN) based stroke detection approach that can be used in linear compressors and any other linear (free-piston) machines. Experimental tests were conducted in a novel linear compressor driven refrigeration system to sample and record voltage, current and displacement. Fast Fourier transform (FFT) analysis was performed on current and voltage signals to extract harmonic terms as inputs of the neural network model to predict the stroke. Six cases with different numbers of harmonic term for current and voltage were compared. Both the mean squared errors and correlation coefficients are significantly improved with the increase of harmonic terms from one to three. However, small difference is indicated between the cases with three and six terms. Best percentage error distribution was achieved in the case with six harmonic terms with the majority of percentage errors falling within ±0.7% and a maximum percentage error of 3.5%. It can be concluded that the present ANN based stroke prediction approach can be effectively adopted for linear compressors without expensive displacement sensors. This is a key step towards the commercialization of linear refrigeration compressor technologies.