2.4. Measurement Uncertainty Assistant

The MUA software version 1.0 is used to calculate the contribution of oil temperature to the standard combined measurement uncertainty of the non-iterative algorithm. The software implements the algorithm for simultaneous variations in input variables. This algorithm uses the Monte Carlo (MC) method and the one-at-a-time oil temperature algorithm described in [21].

In Figure 2a, the first variant of the algorithm for simultaneous variations in input variables is shown. This variant varies simultaneously with four input parameters: T, x, y, and z. The number of different values used for PD location is marked with N_MC (the number of MC simulations).

(a) First variant of the algorithm for simultaneous variations in input variables, where the positions of the acoustic emission (AE) sensors remain unchanged; (b) second variant of the algorithm, where the PD position remains unchanged. The current iteration number is marked with n_MC.

In Figure 2b, the second variant of the algorithm is shown. This variant varies simultaneously with 13 input parameters: T, x_Si, y_Si, and z_Si (i = 1,..., 4). The number of different values used for the AE sensors’ locations is marked with N_MC.

In both variants of the algorithm, the number of different values for the oil temperature used to probe the PD location is marked with N. The maximum change in the result to detect the PD location is marked with ∆g_max = max(∆x, ∆y, ∆z). The position of the PD is described by the mean distance from the AE sensors l_sr = (l₁ + l₂ + l₃ + l₄)/4. The result is a set of paired values: ∆T(n, n_MC), ∆gmax(n, n_MC), ∆gmax/∆T(n, n_MC), and l_sr(n_MC), where n = 1,..., N and n_MC = 1,..., N_MC.

The MUA software was written in Visual C# programming language and stored the simulation results in a database. The development tool was Microsoft Visual Studio Community 2019. The user interface of the MUA software displays three tabs. The first tab from left to right is for PD location calculation based on manually inserted input parameters of the non-iterative algorithm. The second and third tab display the significant simulation parameters and valid simulation results of the first and the second simulation, respectively (Figure 3 and Figure 4).

Presentation of the results of the first variant of the algorithm for simultaneous variations in input variables in the MUA software. (a) Calculation and display of significant simulation parameters; (b) display of valid simulation results and selected columns from the database; (c) MC simulation number; (d) the sensors’ arrangement is different depending on the PD position; (e) random positions of PD.

Presentation of the results of the second variant of the algorithm for simultaneous variations in input variables in the MUA software. (a) Sensors’ positions are randomized; (b) PD location remains unchanged; (c) the results of one-at-a-time oil temperature algorithm within the single MC simulation.