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Abstract The article presents results of tests performed with the use of a ship engine of the type Sulzer 6AL20/24. The goal of the tests was to create and verify an identification procedure for the analyzed object’s reliability states to be used without interfering with the object operation processes. The proposed method is based on an analysis of vibrations and noise generated during the engine operation, which are considered to be the most significant diagnostic signals. The signals of the engine vibrations and noise recorded during the engine operation on a laboratory test stand have been analyzed in the time domain. A number of the recorded signal characteristics are calculated. The characteristics are statistically analyzed in order to choose those which can provide the basis for the identification of reliability states. Next, based on the spaces of ability and inability, states are formulated.

Abstract Road surface characteristics directly influence vehicle safety and performance, and its knowledge can be instrumental to road transportation system safety. This work focuses on the development of a test setup, which was utilized for real-time implementation of a road surface identification algorithm based on the acceleration response of an intelligent tire. Analysis of frequency domain data was used to leverage the tire-road contact information being relayed through the acceleration data. A signal processing algorithm was developed to separate each tire revolution, analyze it in real time, and convert it to the frequency domain in real time. In the end, the performance of the setup was validated with results from the literature, and the distinguishing signature possessed by each surface was used to categorize different terrains into the respective surface categories (Dry Asphalt, Wet Asphalt, Concrete) in real time.

Abstract Few studies have investigated the vibration characteristics of motorcycle tires. Tires have high damping characteristics; therefore, resonance peaks do not appear clearly, and it is difficult to identify the existence of modes in a high-frequency region having many modes. A similar phenomenon occurs in automobile tires; therefore, the circumferential mode reduction method is used to reduce the number of degrees of freedom of the frequency response function. This method is specialized for periodic shell structures under the assumption that the circumferential mode shape can be represented by trigonometric functions. However, the applicability of this method to the experimental modal analysis of motorcycle tires with a double curvature remains unclear. Therefore, in this study, we verify whether this method can be applied to periodic shell structures with a double curvature. Next, we try to understand the modal characteristics of motorcycle tires.

Abstract Aiming at the problem of low recognition rate and slow speed caused by the small proportion of key area information in feature vectors of original Pyramid Histogram of Gradients (PHOG) features, an improved feature extraction method of PHOG is proposed. The PHOG feature extraction method is combined with edge feature enhancement method based on Census transform to extract feature vectors of fasteners, and dimensionality reduction is processed by Kernel Principal Component Analysis (KPCA) method to reduce the interference of redundant information. The vector is inputted into the support vector machine for training in order to get the classifier model and realize the automatic identification of the fastener’s state. The simulation results show that compared with the traditional PHOG method, this feature extraction method improves the false detection rate by 2.7%, and the complexity of the algorithm is greatly reduced.

Abstract Damage is accumulated by vehicles as they travel. Current damage methods allow for the total accumulated damage to be identified; however, they do not allow for identification of the road segments that induce the largest component of the damage. The objective of this article is to develop a measure, Localized Pseudo Damage (LPD), which defines the amount of damage each individual road excitation contributes to the total accumulated pseudo damage. A novel theoretical development of LPD along with analytical and discrete simulation analyses is presented. The results show that the LPD is causal and correctly identifies the location and magnitude of damaging events. This is further demonstrated with the application of the method on a real road surface.

Abstract Parameter identification is an important part of tire model development. The prediction performance of a tire model highly depends on the identified parameter values of the tire model. Different optimization algorithms may yield different tire parameters with different computational accuracy. It is essential to find out which optimization algorithm is most likely to generate a set of parameters with the best prediction performance. In this study, four different MATLAB® optimization algorithms, including fminsearchcon, patternsearch, genetic algorithm (GA), and particleswarm, are used to identify the parameters of a newly proposed in-plane flexible ring tire model. The reference data used for parameter identification are obtained through a ADAMS FTire® virtual cleat test. After parameters are identified based on above four algorithms, their performances are compared in terms of effectiveness, efficiency, reliability, and robustness.