In this paper, the aero-engine test with inter-shaft bearing fault is carried out, and a dataset is proposed for the first time based on the vibration signal of rotors and casings. First, a test rig based on a real aero-engine is established, driven by motors and equipped with a lubricating system. Then, the aero-engine is disassembled and assembled following the specification process, and the inter-shaft bearing with artificial fault is replaced. Next, the aero-engine test is conducted at 28 groups of high and low pressure speeds. Six measuring points are arranged, including two displacement sensors to test the displacement vibration signals of the low pressure rotor and four acceleration sensors to test the acceleration vibration signals of the casing. The test results are integrated into an inter-shaft bearing fault dataset. Finally, based on the dataset in this paper, frequency spectrum, envelope spectrum, CNN, LSTM and TST are used for fault diagnosis, and the results are compared with those of CWRU and XJTU datasets. The results show that the characteristic fault frequency cannot be found directly in the spectrum and envelope spectrum corresponding to this paper's dataset but in CWRU and XJTU datasets. Using CNN, LSTM and TST for fault diagnosis of the dataset in this paper, the accuracy is 83.13%, 85.41% and 71.07%, respectively, much lower than the diagnosis results of CWRU and XJTU datasets. It can be seen that the dataset in this paper is closer to the actual fault diagnosis situation and is a more challenging dataset. This dataset provides a new benchmark for the validation of fault diagnosis methods. Mendeley data: https://github.com/HouLeiHIT/HIT-dataset.

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The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals. In this paper, a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method, where the crack’s periodic opening and closing pattern and different degrees of crack depth are considered. Then, the dynamic response is obtained by the harmonic balance method. By adjusting the crack parameters, the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots. The analysis results show that the first critical speed, first subcritical speed, first critical speed amplitude, and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis. Based on this, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input. Test results show that the proposed method has high fault diagnosis accuracy. This research proposes a crack detection method adequate for the hollow shaft rotor system, where the crack depth and position are both unknown.