The study aimed to examine the use of Geomagnetic Anomaly Detection (GAD) to locate the buried ferromagnetic pipeline defects without exposing them. However, the accuracy of GAD is limited by the background noise. In the present work, we propose an approximate entropy noise suppression (AENS) method based on Variational Mode Decomposition (VMD) for detection of pipeline defects. The proposed method is capable of reconstructing the magnetic field signals and extracting weak anomaly signals that are submerged in the background noise, which was employed to construct an effective detector of anomalous signals. The internal parameters of VMD were optimized by the Scale–Space algorithm, and their anti-noise performance was compared. The results show that the proposed method can remove the background noise in high-noise background geomagnetic field environments. Experiments were carried out in our laboratory and evaluation results of inspection data were analysed; the feasibility of GAD is validated when used in the application to detection of buried pipeline defects.

Shaft-stator rub and cracks on rotors, which have devastating effects on the industrial equipment, cause nonlinear and in some cases chaotic lateral vibrations. On the other hand, vibrations caused by machinery faults can be torsional in cases such as rub. Therefore, a combined analysis of lateral and torsional vibrations and extraction of chaotic features from these vibrations is an effective approach for rotor vibration monitoring. In this study, lateral and torsional vibrations of rotors have been examined for detecting cracks and rub. For this purpose, by preparing a laboratory model, the lateral vibrations of a system with crack and rub have been acquired. After that, a practical method for measuring the torsional vibrations of the system is introduced. By designing and installing this measurement system, practical test data were acquired on the laboratory setup. Then, the method of phase space reconstruction was used to examine the effect of faults on the chaotic behaviour of the system. In order to diagnose the faults based on the chaotic behaviour of the system, largest Lyapunov exponent (LLE), approximate entropy (ApEn) and correlation dimension were calculated for a healthy system and also for a system with rub and a crack. Finally, by applying these parameters, the chaotic feature space is introduced in order to diagnose the intentionally created faults. The results show that in this space, the distinction between the various defects in the system can be clearly identified, which enables to use this method in fault diagnosis of rotating machinery.