G. T. Zheng

Bio: G. T. Zheng is an academic researcher from Beihang University. The author has contributed to research in topics: Signal processing & Noise reduction. The author has an hindex of 1, co-authored 1 publications receiving 30 citations.

Internal Combustion Engine Noise Analysis With Time-Frequency Distribution

Abstract: An analysis procedure, using the time-frequency distribution, has been developed for the analysis of internal combustion engine noise signals. It provides an approach making use of advantages of both the linear time-frequency distribution and the bilinear time-frequency distribution but avoiding their disadvantages. In order to identify requirements on the time-frequency analysis and also correlate a time-frequency analysis result with noise sources, the composition of the noise signal is discussed first. With this discussion, a mathematical model has been suggested for the noise signal. An example of identifying noise sources and detecting the abnormal condition of an injector with the noise signal time-frequency distribution for a diesel engine is also provided.

Multi-Fault Rapid Diagnosis for Wind Turbine Gearbox Using Sparse Bayesian Extreme Learning Machine

Abstract: In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert–Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the single- and simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques.

Combustion diagnosis via block vibration signal in common rail diesel engine

Abstract: This article presents a diagnostic technique in which nonintrusive measurements are used with the aim of indirect characterization of the combustion process of an internal combustion diesel engine....

Condition Monitoring of Internal Combustion Engine Using EMD and HMM

Abstract: The acoustic signature of an internal combustion (IC) engine contains valuable information regarding the functioning of its components. It could be used to detect the incipient faults in the engine. Acoustics-based condition monitoring of systems precisely tries to handle the questions and in the process extracts the relevant information from the acoustic signal to identify the health of the system. In automobile industry, fault diagnosis of engines is generally done by a set of skilled workers who by merely listening to the sound produced by the engine, certify whether the engine is good or bad, primary owing to their excellent sensory skills and cognitive capabilities. It would indeed be a challenging task to mimic the capabilities of those individuals in a machine. In the fault diagnosis setup developed hereby, the acoustic signal emanated from the engine is first captured and recorded; subsequently the acoustic signal is transformed on to a domain where distinct patterns corresponding to the faults being investigated are visible. Traditionally, acoustic signals are mainly analyzed with spectral analysis, i.e., the Fourier transform, which is not a proper tool for the analysis of IC engine acoustic signals, as they are non-stationary and consist of many transient components. In the present work, Empirical Mode Decomposition (EMD) and Hidden Markov Model (HMM)- based approach for IC engine is proposed. EMD is a new time-frequency analyzing method for nonlinear and non-stationary signals. By using the EMD, a complicated signal can be decomposed into a number of intrinsic mode functions (IMFs) based on the local characteristics time scale of the signal. Treating these IMFs as feature vectors HMM is applied to classify the IC engine acoustic signal. Experimental results show that the proposed method can be used as a tool in intelligent autonomous system for condition monitoring and fault diagnosis of IC engine.

The vibroacoustic methods and their application in the diagnostics of the combustion engine's roller-bearing fittings

Abstract: The roller-bearings are the most widely used appliances of the engine's fittings. The regular use of the rollerbearings leads to their degradation and subsequently to their damage. In extreme situations, the damaged roller-bearing can come to a standstill leading to the engine's failure. As for the maintenance of the engine, it would be crucial to work out the methods of supporting the diagnosis of the engine's fittings and its elements. The methods would enable to determine the condition of measure without the need of the disassembly. The vibroacoustic methods present the greatest possibilities in that field. The authors attempted to realize that task for a chosen element of the combustion engine's fittings in a car. The article presents the results of the conducted research and the study concerning the diagnosis of the bearing's timing belt tensioner pulley. The investigation was conducted for new roller-bearings as well as damaged. Different values of tension in investigations were applied by timing belt, which were placed for help of instrument PR-20. The accelerations of radial vibrations of screw fixing the tensioner pulley were measured during experiments. The recording of accelerations of vibrations was done for different constant engine speeds. The results of the research were analyzed by using software MatLab. The study indicated the measure sensitive to the changes of the technical condition of the tested element. The findings obtained confirm the usefulness of the presented method which in the form of a suitable algorithm may use as the basis for creating a diagnostic device.