Evidence-theory-based kinematic uncertainty analysis of a dual crane system with epistemic uncertainty

An evidence-theory-based interval perturbation method (ETIPM) and an evidence-theory-based subinterval perturbation method (ETSPM) are presented for the kinematic uncertainty analysis of a dual cranes system (DCS) with epistemic uncertainty. A multiple evidence variable (MEV) model that consists of evidence variables with focal elements (FEs) and basic probability assignments (BPAs) is constructed. Based on the evidence theory, an evidence-based kinematic equilibrium equation with the MEV model is equivalently transformed to several interval equations. In the ETIPM, the bounds of the luffing angular vector (LAV) with respect to every joint FE are calculated by integrating the first-order Taylor series expansion and interval algorithm. The bounds of the expectation and variance of the LAV and corresponding BPAs are calculated by using the evidence-based uncertainty quantification method. In the ETSPM, the subinterval perturbation method is introduced to decompose original FE into several small subintervals. By comparing results yielded by the ETIPM and ETSPM with those by the evidence theory-based Monte Carlo method, numerical examples show that the accuracy and computational time of the ETSPM are higher than those of the ETIPM, and the accuracy of the ETIPM and ETSPM can be significantly improved with the increase of the number of FEs and subintervals.

Hybrid Compound Function/Subinterval Perturbation Method for Kinematic Analysis of a Dual-Crane System With Large Bounded Uncertainty

By introducing the subinterval perturbation method (SIPM), a hybrid compound function/subinterval perturbation method (HCFSPM) is presented for a dual-crane system (DCS) with large interval variables. The HCFSPM employs the SIPM to decompose a large interval variable into several subinterval variables with small uncertain levels. The interval kinematic compound function vectors and their inverses are approximated by the first-order Taylor and Neumann series, respectively. Based on the monotonic technique, the bounds of original luffing angle vectors are derived. Compared with the first-order compound function/interval perturbation method and the Monte Carlo method, numerical examples verify the effectiveness of the HCFSPM at conducting uncertain kinematic analysis of the DCS, especially when it comes to large uncertain levels.

Modified First-Order Compound Function-Based Interval Perturbation Method for Luffing Angular Response of Dual Automobile Crane System With Interval Variables

The accuracy of conventional crane engineering problems with bounded uncertainty is limited to cases where only first-order terms are retained. However, the impact of high-order terms on the luffing angular response (LAR) may be significant when it comes to compound functions. A modified first-order compound-function-based interval perturbation method (MFCFIPM) is proposed for the prediction of the LAR field of a dual automobile crane system (DACS) with narrowly bounded uncertainty. In an interval model, all uncertain variables with bounded uncertainty comprise an interval vector. The equilibrium equations of the interval LAR vectors of the DACS are established based on the interval model. The MFCFIPM employs the surface rail generation method to expand the compound-function-based vectors. A modified Sherman–Morrison–Woodbury formula is introduced to analyze the impact of the high-order terms of the Neumann series expansion on the LAR field. Several numerical examples are presented to verify the accuracy and the feasibility of the MFCFIPM. The results show that the MFCFIPM can achieve a better accuracy than the first-order compound-function-based interval perturbation method and a higher efficiency than the Monte Carlo method for the LAR field problem with narrow interval variables. The effects of different numbers of interval variables on the LAR field by the MFCFIPM are also investigated.

Hybrid Function-Based Moment Method for Luffing Angular Response of Dual Automobile Crane System With Random and Interval Parameters

A hybrid uncertain parameter model (HUPM) is introduced to predict the luffing angular response (LAR) field of the dual automobile cranes system (DACS) with random and interval parameters. In the model, all random parameters with specified probabilistic distributions comprise a random vector, while all interval parameters with determined bounds comprise an interval vector. A hybrid uncertain LAR equilibrium equation is established, and a novel approach named as hybrid perturbation compound function-based moment method is proposed based on the HUPM. In the hybrid perturbation compound function-based moment method, the expression of LAR is developed according to the random interval perturbation compound function-based method. More, by using the random interval compound function-based moment method and the monotonic technique, the expectations and variances of the bounds for LAR are calculated. Compared with the hybrid Monte Carlo method (HMCM) and interval perturbation method (IPM), numerical results on different uncertain cases of the DACS demonstrate the feasibility and efficiency of the proposed algorithm. The proposed method is proved to be an effective engineering method to quantify the effects of hybrid uncertain parameters on the LAR of DACS.

An Iterative Dimension-Wise Approach to the Structural Analysis with Interval Uncertainties

The structural analysis is inevitably surrounded with uncertainties and the interval analysis is a favorable method if insufficient data is available on uncertainties. The accuracy of current interval analysis methods including the interval perturbation method (IPM), subinterval perturbation method (SIPM) and dimension-wise approach (DWA) depends on a reference point (RP), e.g., the expansion point in IPM, for some problems due to ignoring the co-operative effects of multiple interval inputs on the response. To this end, an iterative dimension-wise approach (IDWA) is proposed. Either the minimal or maximal input vector of the response is identified as an RP by a global update in which a novel RP is dimension-wisely assembled by the minimal or maximal points of all sectional curves of the response surface at a previous RP through a local update. The interval response is calculated by deterministic solvers at the minimal and maximal input vectors. An acoustic analysis problem is studied eventually to validate the effectiveness of the proposed method, from which conclusions are drawn.

Efficient Midfrequency Analysis of Built-Up Structure Systems with Interval Parameters

To improve the efficiency of midfrequency analysis of built-up structure systems with interval parameters, the second-order interval and subinterval perturbation methods are introduced into the hybrid finite element/statistical energy analysis (FE/SEA) framework in this paper. Based on the FE/SEA for built-up structure systems and the second-order interval perturbation method, the response variables are expanded with the second-order Taylor series and nondiagonal elements of the Hessian matrices are neglected. Extreme values of the expanded variables are searched by using efficient search algorithm. For large parameter intervals, the subinterval perturbation method is introduced. Numerical results verify the effectiveness of the proposed methods.

Interval Perturbation Method to Structural Non-Probabilistic Reliability Analysis

In structural non-probabilistic reliability analysis, the uncertain structural parameters are assumed to be the interval parameters. The commonly used probability model will lose accuracy when there is not enough experimental date for the reliability analysis. Conversely, the interval model only requires the upper and lower bound of the uncertain variable, which is more reasonable compared with the probabilistic model. The interval perturbation method is applied in this paper to compute the non-probabilistic reliability index, where the interval expansion problem has been effectively controlled. The precision of computing the reliability index is effectively improved, solving the problem of the non-probabilistic reliability index in a new way. The numerical results prove that this method is effective and feasible.