Fault diagnosis of bearings is a crucial part of the maintenance process of the rotary machinery. Extracting the cyclic characteristics of the impact force is of significant importance for the bearing diagnosis. To highlight the fault features from signals combined with heavy background noise, a novel approach for bearing fault diagnosis based on the short-time processing is proposed. Fault signals are regarded as periodic impulse response signals. Firstly, a vibration signal is band-pass filtered with a subsequent spectral analysis. Then we integrate the energy of the filtered signal with a constant length, and the natural logarithm is considered to obtain the energy curve. The energy curve is a straight decaying curve, and its spectral energy is more concentrated on the fault characteristic frequency compared with envelope. Finally, the fault characteristic frequency of the bearing is found by the spectral analysis of the energy curve. The effectiveness of the proposed method is verified by simulation and experiments. The harmonics and sidebands in logarithmic energy spectrum are suppressed well, and the fault characteristic frequency is highlighted. Comparison of the proposed method with Hilbert envelope method shows that the proposed method can highlight the fault characteristic frequency.

The synthesised sound of a car engine is used to alert people to the approach of an electric vehicle, to personalise the sound of an engine and for virtual reality. A methodology for synthesising engine sound based on concatenating samples is proposed. First, using filtering, the engine sound is decomposed into a combination of low-frequency harmonics that depend on the engine speed and high-frequency narrowband amplitude-modulated signals. The high-frequency signals are modulated by the harmonics that depend on the engine speed. The carrier and envelope of the amplitude-modulated signal are extracted with a Hilbert transform. The decomposed segments are concatenated by overlap smoothing. All the concatenated segments are assembled to form a synthesised sound. Finally, the synthesised sound is evaluated using the cepstrum distance and subjective auditory experiment, and it is compared with the raw engine sound and other synthesised sound.