VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing

Early identification of rolling element defects is always a topic of interest for researchers and the industry. For early fault identification, a simple and effective dynamic degradation monitoring method using variational mode decomposition (VMD) based trigonometric entropy measure is developed. First, vibration signals are obtained and are further decomposed using VMD to obtain various frequency modes. Second, a trigonometric entropy measure is developed to monitor the dynamic change occurring in the health of bearing. Third, trigonometric entropy measure of various VMD modes is computed. Fourth, the variance of measure is computed and two modes having the highest variance are selected for principal component analysis (PCA). Thereafter, PCA of selected measures is carried out. Finally, dynamic degradation monitoring is carried out by observing the trend in the principal component having the highest diverse information. The testing of newly developed VMD based trigonometric entropy measure is carried out on the two different types of data set. One is from XJTU-SY Bearing datasets and another is from the Centre for Intelligent Maintenance Systems. The experimental study reveals that the proposed method is capable of raising the alarm about the initiation of defects at a very early stage. Compared to existing indicators such as kurtosis, RMS, and Shannon entropy, the proposed method is superior while carrying out defect degradation monitoring.

A New Field Protocol for Monitoring Forest Degradation

Forest degradation leads to the gradual reduction of forest carbon stocks, function, and biodiversity following anthropogenic disturbance. Whilst tropical degradation is a widespread problem, it is currently very under-studied and its magnitude and extent are largely unknown. This is due, at least in part, to the lack of developed and tested methods for monitoring degradation. Due to the relatively subtle and ongoing changes associated with degradation, which can include the removal of small trees for fuelwood or understory clearance for agricultural production, it is very hard to detect using Earth Observation. Furthermore, degrading activities are normally spatially heterogeneous and stochastic, and therefore conventional forest inventory plots distributed across a landscape do not act as suitable indicators: at best only a small proportion of plots (often zero) will actually be degraded in a landscape undergoing active degradation. This problem is compounded because the metal tree tags used in permanent forest inventory plots likely deter tree clearance, biasing inventories toward under-reporting change. We have therefore developed a new forest plot protocol designed to monitor forest degradation. This involves a plot that can be set up quickly, so a large number can be established across a landscape, and easily remeasured, even though it does not use tree tags or other obvious markers. We present data from a demonstration plot network set up in Jalisco, Mexico, which were measured twice between 2017 and 2018. The protocol was successful, with one plot detecting degradation under our definition (losing greater than 10% AGB but remaining forest), and a further plot being deforested for Avocado (Persea americana) production. Live AGB ranged from 8.4 Mg ha–1 to 140.8 Mg ha–1 in Census 1, and from 0 Mg ha–1 to 144.2 Mg ha–1 Census 2, with four of ten plots losing AGB, and the remainder staying stable or showing slight increases. We suggest this protocol has great potential for underpinning appropriate forest plot networks for degradation monitoring, potentially in combination with Earth Observation analysis, but also in isolation.

New Multifeature Information Health Index (MIHI) Based on a Quasi-Orthogonal Sparse Algorithm for Bearing Degradation Monitoring

Data-driven intelligent prognostic health management (PHM) systems have been widely investigated in the area of defective bearing signals. These systems can provide precise information on condition monitoring and diagnosis. However, existing PHM systems cannot identify the accurate degradation trend and the current fault types simultaneously. Given that different fault types have various effects on the mechanical system, the corresponding maintenance strategies also vary. Then, choosing the appropriate maintenance strategy according to the future fault type can reduce the maintenance cost of the equipment operation. Therefore, a multifeature information health index (MIHI) must be developed to trace various bearing degradation trends with various types of faults simultaneously. This paper reports a new quasi-orthogonal sparse project algorithm that can mutually convert the degraded processing feature vector sets (such as spectrum) for each type of fault to orthogonal approximate spatial straight lines. The algorithm builds a MIHI through the spectrum of current state measured points. The MIHI is then transformed by a quasi-orthogonal sparse project algorithm to trace the various bearing degradation trends and recognize the fault type simultaneously. The case study of bearing degradation data demonstrates that this approach is effective in assessing the various degradation trends of different fault types.

Degradation Monitoring in Reinforced Concrete with 3D Localization of Rebar Corrosion and Related Concrete Cracking

Corrosion of the reinforcement is a major degradation mechanism affecting durability and safety of reinforced concrete (RC) structures. As the corrosion process starts internally, it can take years before visual damage can be noticed on the surface, resulting in an overall degraded condition and leading to large financial costs for maintenance and repair. The acoustic emission (AE) technique enables the continuous monitoring of the progress of internal cracking in a non-invasive way. However, as RC is a heterogeneous material, reliable damage detection and localization remains challenging. This paper presents extensive experimental research aiming at localizing internal damage in RC during the corrosion process. Results of corrosion damage monitoring with AE are presented and validated on three sample scales: small mortar samples (scale 1), RC prisms (scale 2), and RC beams (scale 3). For each scale, the corrosion process was accelerated by imposing a direct current. It is found that the AE technique can detect damage earlier than visual inspection. However, dedicated filtering is necessary to reliably localize AE events. Therefore, AE signals were filtered by a newly developed post-processing protocol which significantly improves the localization results. On the smallest scale, results were confirmed with 3D micro-CT imaging, whereas on scales 2 and 3, results were compared with surface crack width measurements and resulting rebar corrosion levels.

Non-Destructive Evaluation of Hidden Defects Beneath the Multilayer Organic Protective Coatings Based on Terahertz Technology

An organic protective coating system plays an important role for the corrosion protection of offshore metallic structures. It is of practical importance to detect possible coating defects and evaluate coating performance for corrosion degradation monitoring. Reliable defect identification can provide timely and effective maintenance to avoid serious consequences. This work investigated various multilayer organic coating systems by terahertz pulse imaging technology and aimed to explore the inspection ability of terahertz non-destructive testing (THz NDT) of protective coatings. Several types of defective coating samples were measured. The comparison of measurements obtained for sites with and without defects was provided. The changes in the signals caused by the presence of defects were explained. Structural analysis, quantitative evaluation, and defect identification were carried out in detail. The results of measurements showed that corrosion defects, paint bulge defects, and paint detachment defects can be distinguished and identified in combination with the causes of defects. It can provide effective technical guidance for terahertz technology to be gradually extended to engineering applications.