## 1 Introduction
[PERSON] _et al._[21] developed the wavelet information entropy method with neural-fuzzy inference system for fault detection in transmission lines and to identify the phases related to the fault of power systems. [PERSON] _et al._[22] identified the misalignment fault of the motor shaft based on multi-scale entropy for wavelet denoising. Many kinds of definitions for information entropy have been developed in detail for some special and concentrated applications, such as fuzzy entropy [23] for customized bacterial foraging, sample entropy [24] for the tool condition detection, hierarchical entropy [25] for biological data analysis, cross entropy [26] for the multi-target tracking, Renyi entropy [13,27] for high-resolution scalar quantization, and so on. Since wavelet transform has the powerful ability to describing un-stationary vibration data in both the time and frequency domains. The definition of wavelet information entropy is developed to describe the dynamic characteristics of the measured vibration data from mechanical equipment. Based on this, a new method based on the ensemble multiwavelet transform and normalized information entropy is studied for the condition identification of aero-engine rotor. In this paper, a fault detection method based on ensemble multiwavelet transform is proposed for assembly looseness fault detection in aero-engine rotors. Benefiting from characteristics of multi-resolution analysis and the multiple basis functions, multiwavelet has great advantage when describing non-stationary vibration data. However, the fixed basis functions of conventional discrete multiwavelet transform, which is not related to vibration data, may decrease the preciseness of fault detection. Moreover, multiwavelet transform does not realize multi-resolution analysis in the high frequency band, which may result in omitting some useful fault feature information. To overcome these limitations, a customized multiwavelet basis function is constructed via symmetric lifting scheme. Then, the measured vibration data from the mechanical equipment is processed by the ensemble multiwavelet transform. The relative energy in a decomposed frequency band of the ensemble multiwavelet transform coefficients means a percentage of the whole signal energy is taken as probability. Normalized information entropy is computed on the basis of the relative energy to reflect the current state of a mechanical system. The proposed method is first applied to the fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used for the engineering application and it successfully identified weak crack fault in a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault detection of aero-engine rotor. The rest of this paper is organized as follows. In Section 2, the theory of multiwavelet is summarized briefly. In Section 3, the multiwavelet symmetric lifting scheme is first described and the customized multiwavelet basis function is constructed based on the symmetric lifting scheme. Then, the proposed method, called ensemble multiwavelet analysis method, is proposed. In Section 4, this method is applied to two cases to demonstrate its usefulness and performance. Conclusions are provided in Section 5.