Variational mode decomposition: mode determination method for rotating machinery diagnosis

Variational mode decomposition (VMD) is a modern decomposition method used for many engineering monitoring and diagnosis recently, which replaced traditional empirical mode decomposition (EMD) method. However, the performance of VMD method specifically depends on the parameter that need to pre-determine for VMD method especially the mode number. This paper proposed a mode determination method using signal difference average (SDA) to determine the mode number for the VMD method by taking the advantages of similarities concept between sum of variational mode functions (VMFs) and the input signals. Online high-speed gear and bearing fault data were used to validate the performance of the proposed method. The diagnosis result using frequency spectrum has been compared with traditional EMD method and the proposed method has been proved to be able to provide an accurate number of mode for the VMD method effectively for rotating machinery applications

Enhanced frequency domain decomposition algorithm: a review of a recent development for unbiased damping ratio estimates

Enhanced frequency domain decomposition (EFDD) is one of OMA methods and has received significant interest from the engineering community involved in the identification of the modal structure. The great attention towards this method is driven by its capability as a user-friendly and fast processing algorithm. However, this method has drawbacks in providing accurate identification of damping ratios, despite natural frequencies and mode shapes can be computed through assuredly and reasonably accurate estimates. The exact practical computation of modal damping is still an open issue, often leading to biased estimates since the errors are coming from every step in EFDD procedures and mainly due to signal processing. Thus, the computation of modal damping becomes tremendously vital in structural dynamics because modal damping is one of the critical parameters of resonance. This review aims to provide relevant essential information on modal damping for a reliable estimation, reduce uncertainties and define error bounds. A literature review has been carried out to find the best practice criteria for modal parameter identification, in particular, modal damping ratio

Piston ring assembly for a new natural gas vehicle symmetrical multistage wobble-plate compressor

Natural gas is an alternative fuel of choice in the market today due to the increase in the price of petroleum, as well as out of environmental concerns. Pressure requirement for a natural gas vehicle (NGV) storage tank is 3000 psig (206 bars). Thus, at NGV refueling facilities, the natural gas need to be stored at a higher pressure in order to refuel the NGV at the pressure required. Compressors are needed in the compression process at the refueling facilities. A new compressor design for natural gas refueling appliance has been developed which is the symmetrical multistage wobble-plate compressor. This compressor design is the newest variation of the axial reciprocating piston compressor. The success of the compressor design in compressing gas depends on the piston ring assembly design. Through this paper, the process of designing the piston ring assembly and considerations taken for this new compressor design were explained. The results presented are those from preliminary tests using air on the working fluid. Real tests on natural gas are to be organised utilising all the experience and lesson learnt from that on air

Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed

Low-speed bearing fault diagnosis based on ArSSAE model using acoustic emission and vibration signals

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. However, under low-speed operating conditions, the diagnosis of bearing components remains a problem. In this paper, the adaptive resilient stacked sparse autoencoder (ArSSAE) is proposed to compensate for the shortcomings of conventional fault diagnosis systems at low speed. The efficiency of the proposed ArSSAE model is initially assessed using the CWRU database. Then, the proposed model is evaluated on actual vibration analysis (VA) and acoustic emission (AE) signals measured on a bearing test rig at low operating speeds (48-480 rpm). Overall, the analysis demonstrates that the ArSSAE model is able to perform an accurate diagnosis of bearing components under low-speed conditions

Automated harmonic signal removal technique using stochastic subspace-based image feature extraction

This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal

Hysteresis Modelling of Pneumatic Artificial Muscle using General Cubic Equation and Factor Theorem Prediction Method / Mohd Azuwan Mat Dzahir...[et al.]

Due to inherent hysteresis in a pneumatic artificial muscle, the accompanying control of this compliant actuator becomes more complicated. In literature, only a few implementations of the hysteresis modelling in the associated position control system could be found. In addition, the high complexity of the contraction-force and contraction-pressure models when accounting for hysteresis effect, the implementation of such model is limited in its use in the pneumatic muscle position control only. However, implementing a complicated control algorithm does not always indicate the best solution that could be used to control the pneumatic artificial muscles. There are arguments in the field of rehabilitation robotics regarding what was the best control system to the orthotic problem for rehabilitation. It is preferred that the control system should be simplified as much as possible; multiple sensors and impedances are only increase the complexity of the control system. Rather than using a very complicated algorithm for control system of the pneumatic artificial muscle, a simple and noble prediction method using general cubic equation and factor theorem is proposed for the hysteresis modelling at different loads. The methodology used to establish the hysteresis modelling and prediction method of the pneumatic artificial muscle are as follows; first is the characterization of the pneumatic artificial muscle at different loads and pressures; second is to develop a prediction method for generating constraint models of hysteresis data at a different loads using general cubic equation and factor theorem; third is to establish a simple theorem or algorithm to extract the hysteresis models (i.e., contraction and expansion) of a pneumatic artificial muscle at different loads based on the generated constraint models; and the final stage of the research is to obtain the hysteresis models at different loads. The generated hysteresis models and hysteresis data obtained from experimental study were compared to verify the reliability of the proposed hysteresis modelling prediction method. The simulation results shows that the hysteresis modelling was able to generate an apropriate constraint models at a different loads

Simulation of lamb wave reflections at plate edges using the semi-analytical finite element method

In typical Lamb wave simulation practices, effects of plate edge reflections are often not considered in order to simplify the wave signal interpretations. Methods that are based on infinite plates such as the semi-analytical finite element method is effective in simulating Lamb waves as it excludes the effect of plate edges. However, the inclusion of plate edges in a finite plate could render this method inapplicable, especially for transient response simulations. Here, by applying the ratio of Lamb mode reflections at plate edges, and representing the reflection at plate edges using infinite plate solutions, the semi-analytical finite element method can be applied for transient response simulation, even when the plate is no longer infinite

Membrane carrier wave function in the modeling of lamb wave propagation

Wave propagation in plates are multi-modal and dispersive by nature. Their behavior is highly dependent on the material properties. In homogeneous isotropic plates Lamb modes can be grouped into symmetric and anti-symmetric modes and they are decoupled from the shear modes. Due to the material isotropy, Lamb wave’s propagation behavior is not dependent on the propagation direction. This property can be used to find analytical solutions for the field of displacements in the frequency domain. In this paper, series of numerical simulations on the Lamb wave propagation in homogeneous isotropic are presented. The concept of the membrane carrier wave is used together with integral transforms in the space domain, and analytical expressions are found for the response of a homogeneous isotropic plate under different load regimes. We considered line forces and axisymmetric loads applied to the plate. The procedure can be applied to other types of load distributions