Accurate detection and quantitative evaluation of defects and damage in metal plates is a crucial task in a range of technological applications, such as maintaining the integrity, enhancing the safety, and assuring the reliability of structures. There is scope for improving eddy current testing methods by incorporating compressive sensing (CS) in the inspection process. The key scientific problems in eddy current imaging of defects of metal plates are sparse representations and transform domain mapping, sparse testing constraints, and sparse image reconstruction. The main research content of this paper is as follows. We first provide basic theory based on research of sparse representations, transform domain mapping, sparse matrices, sparse transform matrices, and signal recovery a priori errors. We then propose information-recovery methods for completing compressive sensing. Third, we establish an experimental system for validating theories and methods. Finally, we establish theories and methods for eddy current imaging of metal plates.

The unmanned helicopter exhibits a complex and nonlinear dynamic behavior and open-loop unstable. This paper describes a attitude control method based on adaptive output feedback for an unmanned helicopter. First, it is assumed that the controlled system satisfies the output feedback linearization conditions. Second, the approximate model of the system is considered as the diffeomorphism of the system. Then, a linear controller and adaptive neural networks are designed to cancel the model errors produced by nonlinear, uncertainty and disturbance. Finally, the boundedness of tracking errors and weight errors are studied with Lyapunov stability theorem. The application results of an unmanned helicopter show that the proposed controller can not only cancel the dynamical error effectively but also improve tracking performance of the attitude control system.

Aiming at the detection problem of blockage in urban water supply pipelines and drainage pipelines, also the problem to distinguish commonly used pipe components such as lateral connection from the actual blocking conditions. A method based on dual-tree complex wavelet transform and Safe Semi-Supervised Support Vector Machine for blockage recognition is proposed in this paper. The first step of this method is to decompose the acoustic signals obtained from the pipeline by the dual-tree complex wavelet transform, and then convert the acquired components into Sound pressure level. Secondly, the pulse factor and the average acoustic energy density are extracted respectively from the effective components as acoustical features. Finally, the S4VM classifier is applied to cluster and label the untrained data, furthermore the different degree of the blocking is able to identify as well as the pipe components.

Aiming at the problem that the vibration signal of the check valve has background noise and low fault recognition rate, a signal characteristics extraction method based on variational mode decomposition and permutation entropy was proposed. The extreme learning machine was used for fault recognition. Firstly, the check valve vibration signal was decomposed by the variational mode decomposition, and the intrinsic mode functions were obtained in different scales. Secondly, the permutation entropy of each intrinsic mode function was calculated and used to compose the multiscale feature vector. Finally, the high-dimensional feature vector was input to the extreme learning machine for check valve fault diagnosis. The comparison is made with EEMD and LCD (local characteristic-scale decomposition). The experimental results show that the method can effectively identify the fault type of the check valve.

Regarding the active acoustical method of determining the blocking status in buried pipe using low frequency sound waves as excitation, it has difficulties in distinguishing the different degrees of blocking and eliminating the interference from the pipe components such as lateral connection, a novel approach to identify the blocking fault using the Sparse Representation for Classification (SRC) enhanced by variational mode decomposition was proposed in this paper. Firstly, the acoustic signals collected in the pipeline were decomposed by variational mode decomposition, then sample entropy characteristic parameters were extracted from each component. Secondly, the feature vectors that can effectively represent the signals were used to construct the dictionary and the sparse representation classifier. The experimental results had shown that the method could effectively identify the blocking status in different degrees, the lateral connection could also be discriminated accurately in the pipeline under a small number of samples, which has certain practical value for engineering applications.