In practice, the operational conditions of rotating machinery are often characterised by variations in rotational speeds and loads, making rotating machinery and its key components, such as gears, bearings, or shafts, easily subject to failure. To guarantee the safety of the operating equipment, there is an urgent need to detect and diagnose the incipient failure of rotating machines, so that an accurate warning can be given and suitable maintenance can be scheduled in advance.

In recent decades, increasing attention has been given to fault detection and diagnostics of rotating machinery. In general, the occurrence of failure can induce nonlinear and nonstationary characteristics to the measured vibration signal, and so analysing the nonlinearity and nonstationarity of vibrations can help reveal the features of the fault. Therefore, multiple techniques have been developed to process vibration signals with consideration to the non-linear and non-stationary properties of the vibrations, such as wavelet transform, empirical mode decomposition, intrinsic time-scale decomposition, local characteristic scale decomposition, variational mode decomposition, and time-frequency distribution. These techniques have made significant contributions to the development of machine condition monitoring. However, the harsh working environment brings in more interference to the measured vibrations, which significantly increases the complexity of the measured vibration signal. As a consequence, this represents a significant challenge for fault detection and diagnostics under variable operation conditions, especially for incipient failures, such as cracks or mild wear.

The aim of this Special Issue is to address the above-mentioned issues by promoting the development of fault diagnosis methods for rotating machinery based on data processing techniques such as adaptive mode decomposition. We hope to attract a broad range of research on weak fault feature extraction based on various signal processing methods, local and composite fault detection, and fault diagnosis under variable working conditions. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Empirical mode decomposition and its variants for signal processing
• Empirical mode decomposition-based machinery fault diagnosis
• Applications of local mean decomposition and its variants
• Machinery fault diagnosis based on variational mode decomposition and its variants
• Fault diagnosis for rolling bearings, gears, and gearboxes based on intrinsic time-scale decomposition and its variants
• Local characteristic-scale decomposition-based machinery fault diagnosis
• Time-frequency distribution theories and applications
• Empirical wavelet transform-based mechanical fault diagnosis
• Improved envelope spectrum-based mechanical fault diagnosis
• Wavelet transform related method-based fault diagnosis cases
• Instantaneous amplitude and frequency estimation with applications in machinery fault diagnosis
• Introduction of newly adaptive mode decomposition methods with applications in special machines