An Investigation into the Acoustic Emissions of Internal Combustion Engines with Modelling and Wavelet Package Analysis for Monitoring Lubrication Conditions

Online monitoring of the lubrication and friction conditions in internal combustion engines can provide valuable information and thereby enables optimal maintenance actions to be undertaken to ensure safe and efficient operations. Acoustic emission (AE) has attracted significant attention in condition monitoring due to its high sensitivity to light defects on sliding surfaces. However, limited understanding of the AE mechanisms in fluid-lubricated conjunctions, such as piston rings and cylinder liners, confines the development of AE-based lubrication monitoring techniques. Therefore, this study focuses on developing new AE models and effective AE signal process methods in order to achieve accurate online lubrication monitoring. Based on the existing AE model for asperity–asperity collision (AAC), a new model for fluid–asperity shearing (FAS)-induced AE is proposed that will explain AE responses from the tribological conjunction of the piston ring and cylinder. These two AE models can then jointly demonstrate AE responses from the lubrication conjunction of engine ring–liner. In particular, FAS allows the observable AE responses in the middle of engine strokes to be characterised in association with engine speeds and lubricant viscosity. However, these AE components are relatively weak and noisy compared to others, with movements such as valve taring, fuel injection and combustions. To accurately extract these weaker AE’s for lubricant monitoring, an optimised wavelet packet transform (WPT) analysis is applied to the raw AE data from a running engine. This results in four distinctive narrow band indicators to describe the AE amplitude in the middle of an engine power stroke. Experimental evaluation shows the linear increasing trend of AE indicator with engine speeds allows a full separation of two baseline engine lubricants (CD-10W30 and CD-15W40), previously unused over a wide range of speeds. Moreover, the used oil can also be diagnosed by using the nonlinear and unstable behaviours of the indicator at various speeds. This model has demonstrated the high performance of using AE signals processed with the optimised WPT spectrum in monitoring the lubrication conditions between the ring and liner in IC engines.

Improved Method of High Voltage Switchgear Temperature Prediction Based on WNN

Based on WNN, an improved model of High Voltage Switchgear temperature prediction is built. The model is optimized with hard threshold de-noising with wavelet package analysis, made an ultra-short term prediction loosely combines Elman neural network. However, the characteristics of the original signal are reserved and the noise interference is mostly eliminated. The experiment results show the processed sample as input one improves the prediction accuracy of Elman Network and reduces the root-mean-square error. And the prediction values show good coincidence to the measured ones that improves the reliability of early warning.

Study on the Identification of Experimental Chaotic Vibration Signal for Nonlinear Vibration Isolation System

In order to identify experimental chaotic vibration signals correctly, the measured data were analyzed by applying the methods of Poincaré section, return map, and phase space reconstruction. However, the nonlinear time series analysis based on phase space reconstruction is complex and time-consuming for large quantities of experimental signals. Besides, especially when the signal identification process should be completed online, the conventional method is unable to meet the requirements. The energy distribution features of signals in different frequency bands were extracted with the wavelet package analysis method, and the important characteristic vectors for chaos identification were provided. These methods were verified with numerical simulation first in this paper. Then, the nonlinear vibration system based on an air spring isolator was designed, which exhibits different responses with different parameters. In the experiment, the wavelet package technology and neural network were applied to identify the system behavior; results showed that the vibration system exhibited chaotic responses under special parameter ranges, and the parameter variation law was concluded, which is the foundation of linear spectra isolation for chaotic vibration control technology.

Identification of Brittle-Ductile Material Removal Characteristics in Ultrasonic Aided Lapping of Engineering Ceramics

For the precision engineering ceramics parts, semi-ductile regime machining technologies are usually adopted to obtain the final surface. It is very important to research the brittle-ductile material removal characteristics and corresponding monitoring technology in ultrasonic aided lapping of engineering ceramics to obtain better surface quality. A series of lapping tests were carried out in the paper and the influence law of lapping parameters on the ductile percentage of ultrasonic lapped surface was achieved, which enables to off-line identify the material removal characteristics. Though the material removal characteristics can be online judged through monitoring the ratio of lapping component forces qualitatively, the monitoring effect is worse just because of lower response frequency of dynamometer and the relativity of lapping force. In this paper, a novel online monitoring technology of material removal characteristics is put forward based on AE technology. Through the wavelet package analysis of AE signals, the discrimination index and standard of material removal characteristics can be obtained. It is proved that this technology can realize online identification of the brittle-ductile material removal characteristics in ultrasonic aided lapping of engineering ceramics effectively.