Yixiang Huang

Research Article: A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion

The development of efficient on-line data processing and decision support algorithms is one of future trends of expert systems for machine condition monitoring research. This paper contributes to a lean model for machine performance assessment by combining an efficient signal processing algorithm, an effective feature selection criterion, and an intelligent assessment method. In the proposed model, firstly, a second generation wavelet packet transform is used to project raw signals into the wavelet domain; secondly, the Fisher criterion is applied to reduce redundant dimensions; eventually, a fuzzy c-means clustering method is used to assess and classify the performance of mechanical systems. The vibration signals from a rolling element bearing experiment has been used to verify both efficiency and effectiveness of the lean model. Compared with conventional methods, the lean model can reduce the time consumption of feature extraction by 49.7% and storage space or data transfer load related to the feature dimensionality by 97.7%, which indicates a great improvement in efficiency.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

A review of key techniques of vision-based control for harvesting robot

We presented a review on the representative vision schemes for harvesting robots.We reviewed hand-eye coordination techniques and their applications in harvesting robots.We presented some fruit or vegetable harvesting robots and their vision control techniques.We described and discussed the challenges and feature trends for robotic harvesting. Although there is a rapid development of agricultural robotic technologies, a lack of access to robust fruit recognition and precision picking capabilities has limited the commercial application of harvesting robots. On the other hand, recent advances in key techniques in vision-based control have improved this situation. These techniques include vision information acquisition strategies, fruit recognition algorithms, and eye-hand coordination methods. In a fruit or vegetable harvesting robot, vision control is employed to solve two major problems in detecting objects in tree canopies and picking objects using visual information. This paper presents a review on these key vision control techniques and their potential applications in fruit or vegetable harvesting robots. The challenges and feature trends of applying these vision control techniques in harvesting robots are also described and discussed in the review.

Study on the preparation and properties of copolyimides based on hexafluoroisopropylidene bis(3,4-phthalic anhydride) and 1,12-di(4-aminophenoxy)dodecane

Abstract Copolyimides were prepared from hexafluoroisopropylidene bis(3,4-phthalic anhydride) (6FDA), 1,12-di(4-aminophenoxy)dodecane and 4,4′-diaminodiphenylether through a solution co-polycondensation reaction followed by a chemical imidization reaction. It was observed that copolyimides prepared with any diamine molar ratio studied (1,12-di(4-aminophenoxy)dodecane/4,4′-diaminodiphenylether=7/3∽1/9) were soluble in polar organic solvents such as N-methyl-2-pyrrolidone (NMP), N,N-dimethylacetamide (DMA), dimethylformamide (DMF) and m-cresol, tetrahydrofuran (THF) and chloroform, while the corresponding homopolyimides were insoluble. Wide angle X-ray diffraction (WAXD) results show a clear relationship between the crystallinity tendency and the solubility of polyimides. High crystallinity tendency leads to low organo-solubility. The glass transition temperature of a copolyimide was in good agreement with Fox’s equation for random copolymers. The long flexible chains in the backbone of copolyimides are the least thermally stable part.

Relevance Vector Machine Based Gear Fault Detection

Recently, condition monitoring of machinery has become global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. In this paper, a novel fault detection method based on relevance vector machine (RVM) is proposed for gear condition monitoring. Empirical results demonstrated that, using similar training time, the RVM model has shown comparable generalization performance to the popular and state-of-the-art support vector machine (SVM), while the RVM requires dramatically fewer kernel functions and needs much less testing time. The results lead us to believe that the RVM is a more powerful tool for on-line fault detection than the SVM.

Dynamic analysis of wind turbines including nacelle-tower-foundation interaction for condition of incomplete structural parameters

Considering the complexity structure of wind turbines, it is difficult to establish an accurate model for wind turbines. This article proposed an improved model based on the vibration signal analysis to investigate the onshore wind turbine dynamic behavior. The model is formulated using the Euler–Lagrangian approach in which the dynamic interaction effects between the nacelle and the tower and the effects between the tower and the foundation are considered. The nacelle is assumed as a mass with 2 degrees of freedom at the top of a flexible tower. And the tower is considered as a beam with elastic end support. The foundation stiffness can influence the dynamic response of wind turbines due to the soft soils surrounding the tower base. A rotational spring and a lateral spring derived from the foundation are used to reflect the interaction between the tower and the foundation. Finally, a comparison with existing National Renewable Energy Laboratory 5-MW baseline wind turbine model is performed. And a field experiment has been implemented to validate the model. The vibration signal of the nacelle and the tower is analyzed to determine the model parameters. Results show that the proposed model is accurate and practical for dynamic property analysis of wind turbine.

A fuzzy based semi-supervised method for fault diagnosis and performance evaluation

How to automatically deal with the unknown classes or status of a machine is a practical problem in many real-world applications. One of the key solutions is to enable the intelligent models with learning ability. Neither supervised nor unsupervised methods can well handle it. In this paper, we proposed a fuzzy based semi-supervised method to not only make the best of the known knowledge but also category the unknown status in a reasonable way. A roller bearing test validates the proposed method for the purpose of both diagnosis and performance evaluation.

Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

ARM based load and hook measuring and tracking for precision hoist of tower crane

It is important to measure the activities of the hook and the load for precision hoisting and safe operation of a tower crane. Visual monitoring and image recognition are the optimum methods for crane hook activity measuring and precision hoisting. Advanced RISC Machines (ARM) is a promising controller for field measuring and controlling of tower crane, but high real time performance and low computation requirements are required for measuring system implemented using ARM. An ARM-based load and hook measuring and tracking system for tower crane is designed. Selecting lifting rope as the target object, an high-performance Improved Progressive Probabilistic Hough Transform (IPPHT) algorithm is proposed for hook activity measuring and tracking. Compared to Progressive Probabilistic Hough Transform (PPHT) more accurate detection is obtained, and the same time the computation time can be reduced to 20%. It is tested the IPPHT is fitting for ARM based measuring system.

A feature extraction method based on probabilistic Principal components analysis and sampling importance resampling for bearing fault detection

The applications of monitoring the equipment online are often limited by the practical signal processing, limited by storage and transferring capacities. The efficiency is a key problem. Thus, a novel highly efficient feature extraction model for evaluating the equipment performance is proposed, which consists of the probabilistic Principal components analysis and the second generation wavelet analysis with the sampling importance resampling method. It starts by transforming raw signals into the wavelet domain by the second generation wavelet packet analysis. Then a sampling-importance resampling procedure is applied to reduce the redundancy and retain the distribution information. The obtained features are then fed into a probabilistic principal components analysis model to reduce the dimensionality. the proposed model is validated in a rolling element bearing test which shows that it is not only effective in diagnosis, but also may save the processing time of feature extraction, the data transfer bandwidth and the storage space.