In recent years, the variational mode decomposition (VMD) method has been introduced for rotating machinery fault diagnosis. However, the results largely depend on its parameters. When an optimization algorithm is employed to optimize these parameters, the fitness function is critical. In this paper, a new fitness function, envelope sample entropy, is constructed. Based on this, a whale optimized VMD method is proposed for rotating machinery fault diagnosis. First, the vibration signals were decomposed by the optimized VMD method to obtain a series of intrinsic mode functions (IMFs), from which the IMFs containing the main information were selected. Then, features were extracted from the selected IMFs and their dimensions were reduced using the local tangent space alignment method. Finally, support vector machine was adopted for fault identification. Compared with related methods, the experiment results show that the proposed method obtains a higher fault recognition accuracy.

Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory–spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual–spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4–12 Hz) in visual–spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.