Papers by Keyword: Failure Prediction

Abstract: An area in the automotive industry that receives a lot of attention today is the introduction of lighter and more advanced material grades in order to reduce carbon emissions, both during production and through reduced fuel consumption. As the complexity of the introduced materials and component geometries increases, so does the need for more complex failure prediction approaches. A proposed path-independent failure criterion, based on a transformation of the limit curve into an alternative evaluation space, is investigated. Initially, the yield criterion used for this transformation of the limit curve was investigated. Here it was determined that the criterion for the transformation could not be decoupled from the material model used for the simulation. Subsequently, the approach using the transformed limit curve was tested on an industrial case from Volvo Cars, but a successful failure prediction was not obtained.

Abstract: This paper presents experimental and numerical results on the deformations and failureof integral composite T-joints subjected to a realistic combined tensile and bending (mixed mode)load case. For this reason, standard pull-off and mixed mode load cases are experimentally studiedby means of a novel test fixture which keeps the force angle constant to the T-joint’s base and allowsfor repositioning of the specimen in order to minimize constraining forces. Two types of specimenswith varying deltoid radius are investigated. Additionally, kinematically nonlinear numerical simulationsare performed to locate damage onset and deformations of the specimens. It is found that thesimulations are in good agreement with the experimental results.

Abstract: An advanced forming process involving hot forming and cold-die quenching, also known as HFQ®, has been employed to form AA6082 tailor welded blanks (TWBs). The HFQ® process combines both forming and heat treatment in a single operation, whereby upon heating the TWB, it is stamped and held between cold tools to quench the component to room temperature. The material therefore undergoes temperature, strain rate or strain path changes during the operation. In this paper, a finite element model (FEM) was developed to investigate the formability and deformation characteristics of the TWBs under HFQ® conditions. Experimental results, i.e. strain distribution, were used to compare and validate the simulation results. A good agreement between the experiment and simulation has been achieved. The developed temperature, strain rate and strain path dependent forming limit prediction model has been implemented into FE simulation to capture the complicated failure features of the HFQ® formed TWBs. It is found from both experiment and simulation that the forming speed has important effects on the occurrence of failure position, where the failure mode for the 1.5-2 mm TWBs may change from localised circumferential necking to parallel weld necking.HFQ® is a registered trademark of Impression Technologies Ltd.

Abstract: Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.

Abstract: In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.

Abstract: In this paper, we introduce a prediction method for air material failure prediction. Firstly, we studied the feature of air material and previous studies on its failure prediction; we then established an air materiel failure prediction model based on Weibull analysis; then we proposed methods to improve the performance of the model through data processing and model adjustment; then, we used our model to estimate the product life cycle and predict the failure of air material based on a data sample.we not only demonstrate the validity of the model and presents a weibull analysis operation steps through the case analysis. Finally, we made some conclusions of this paper and proposed some suggestions for future research.

Abstract: The railway switch failure prediction for railway signal equipment maintenance plays an important role. The paper put forward railway switch failure prediction algorithm based on least squares support vector machine, and chose five characteristic indexes composed of railway switch failure prediction models characteristic input vectors. It reduces the dimension of input vectors, shorten the least squares support vector machine training time, and use a pruning algorithm to accelerate the computing speed maintaining a good regression performance at the same time. The experiment proved that railway switch failure prediction algorithm has strong self-learning ability and higher prediction accuracy based on least squares support vector machine. And it can accelerate the speed of switch failure prediction and improve the accuracy and reliability of railway switch failure prediction.

Abstract: 172 basic Cessna plane in the process of operation, the production of equipment failure is random, so the evaluation of equipment performance and to predict its failure time to improve the safe operation of the 172 basic plane has important application value. On the plane this complex system, the grey theory combined with 172 basic Cessna plane, the collection of 172 basic aircraft fault information centralized data processing, analysis, prediction model GM (1, 1), through the calculation of the GM model data, and the error precision fitting test, better realize the basic 172 aircraft equipment failure time prediction.

Abstract: This paper mainly studies one kind of failure prediction method on electrical connectors at high temperature. One scheme of high temperature reliability test was provided based on the theory of constant stress accelerated life test. The test device was designed; the test data was analyzed. It can be seen that 1) both the absolute change and the relative change of contact resistance are small for each temperature stress; 2) there is a tendency toward slightly higher value of contact resistance for each low temperature stress; 3) the higher the test temperature stress, the larger the value of contact resistance, but the difference in contact resistance is not very big. 4) The data prediction method such as the gray theory model is helpful for rapid failure prediction of electrical connectors at high temperature.

Abstract: Non-mining fracturing of shaft lining is a special mine geological disaster in Huadong thick alluvium Mining Districts, which is mainly induced by the additional stress result from compressive deformation of the bottom aquifer water loss. On the basis of summarizing the geological conditions and failure characteristics of non-mining fracturing of shaft lining, this paper analyses the size and variation with different influencing factors of additional stress in outer wall through establishing 3-D numerical modeling. The relationship between stress condition of non-mining fracturing of shaft lining and head loss of bottom water-bearing stratum is determined using the classic elastic solution to thick-walled cylinder according to the proceeding modeling results; and the head loss of bottom water-bearing stratum corresponding to the failure limit state of pitshaft damage is obtained.