Search

The aim of this paper is to propose a comparative study between three advanced signal processing methods for the vibratory diagnosis of rotating machines working in industrial conditions. Cyclostationary analysis, empirical mode decomposition (EMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are then applied for the detection of mechanical defects of a turbofan machine in the biggest fertilizer company in Algeria. These methods proved their efficiency for the diagnosis of specific defects, like rolling bearing and gear defects in laboratory test rigs, but their application in industrial field remains limited. The application of these methods on vibratory signals measured in low, medium, and high-frequency range allowed determining the efficiency of each method to diagnose the occurrence of different defects manifested in the three considered frequency ranges. The great advantage of the modulation intensity distribution (MID), and its integration (IMID), obtained from the cyclostationary analysis, is proven compared to the envelope spectra performed from the EMD or the CEEMDAN approaches, especially for defects inducing modulation phenomena. Finally, the main result of this paper is that the advanced signal processing tools can be easily applied on signals measured in industrial environment, and can be extended to detect mechanical defects in real running conditions, more real than those simulated on laboratory test rigs. [ABSTRACT FROM AUTHOR]

The early detection of defects in rotating machines before their severity reaches an intolerable threshold has become the priority of the maintenance crew, hence the need to apply or even adapt new reliable methods that can meet this requirement. In this paper, a hybrid approach based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the optimized wavelet multi-resolution analysis (OWMRA), and the Hilbert transform is proposed. The proposed approach is initially applied on experimental signals measured on faulty bearings of the Machinery Fault Simulator (MFS) test rig. Two types of defects are considered: on the outer race and on the inner race. An industrial application carried out on a production machine validates on a large scale, exceeding that of the laboratory, the proposed method. It was then possible to detect potential faults on a turbo-alternator group GZ 1164.2 operating in real industrial conditions in the largest fertilizer products company in Algeria. The obtained results show the power of the proposed approach to get reconstructed signals less noisy, containing more information and highlighting the precise nature of the defect and its severity in different configurations, regardless of its type. Finally, the main contribution of this study is to show that the application of the proposed approach is very practical on signals measured on real industrial rotating machines to detect the potential mechanical faults. [ABSTRACT FROM AUTHOR]