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In health monitoring of complex mechanical systems such as aircraft engines there are many components whose diagnosis is of great interest for the industry. A conventional way to monitor these components is to collect vibration signals using accelerometers placed in their closest vicinity. However, due to some restrictions such as inaccessibility, it is not always practical to place the accelerometers as such. In many cases, pre-installed instrumentations are used, which are usually inadequate and placed on the carcass of the structure. Nevertheless, even if the accelerometers are positioned very close to the components, they would collect signals not just from one specific component but from other components as well. In this study, we sought to employ frequency-based independent component analysis (ICA) to recover the signals produced by components within a single complex system. In such a case, differences between “blind source separation” and vibration source separation are discussed. A new workaround for the permutation ambiguity encountered in the implication of ICA is proposed. Finally, in order to demonstrate the applicability of the new proposed approach, experimental results carried out on a test bed are presented.

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An obstacle in diagnosis of multicomponent machinery using multiple sensors to acquire vibration data is firstly found in the data acquisition itself. This is due to the fact that vibration signals collected by each sensor are a mixture of vibration produced by different components and noise; it is not evident what signals are produced by each component. A number of research studies have been carried out in which this problem was considered a blind source separation (BSS) problem and different mathematical methods were used to separate the signals. One complexity with applying such mathematical methods to separate vibration sources is that no metric or standard measure exists to evaluate the quality of the separation. In this study, a method based on statistical energy analysis (SEA) is proposed using Fourier transforms and the spatial distance between sensors and components. The principle of this method is based on the fact that each sensor, with respect to its location in the system, collects a different version of the vibration produced in the system. By applying a short time Fourier transform to the signals collected by multiple sensors and making use of a priori knowledge of the spatial distribution of sensor locations with respect to the components, the source of the peaks on the frequency spectra of the signals can be identified and attributed to the components. The performance of the method was verified using a series of experimental tests on synthetic signals and real laboratory signals collected from different bearings and the results confirmed the efficacy of the method.