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Identification of lubricant contamination by biodiesel using vibration analysis and neural network

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Purpose ‐ The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network. Design/methodology/approach ‐ The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions. Findings ‐ Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. On other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade. Research limitations/implications ‐ This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed. Practical implications ‐ Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm. Originality/value ‐ This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.
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Keywords: Bearings; Condition monitoring; Contamination; Crankcase oils; Internal combustion engines; Lubricants; Neural networks; Probabilistic neural network; Vibration

Document Type: Research Article

Publication date: March 2, 2012

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