A major task in mines and even more in underground repositories for nuclear waste is to investigate crack formation for evaluation of rock mass integrity of the host rock. Therefore, in situ acoustic emission (AE) monitoring are carried out in mines as part of geomechanical investigations regarding the stability of underground cavities and the integrity of the rock mass. In this work, the capability of in situ AE monitoring in the context of structural health monitoring (SHM) in mines and in various geological settings will be reported. SHM pointed out, that the AE network is able to monitoring AE activity in rock with a volume up to ${10}^6$ cubicmeter and distances up to 200 m (e.g., 100 m × 100 m × 100 m) in the frequency range of 1 kHz to 150 kHz. Very small AE events with source size in approximately centimeter to millimeter scale are detected. The results show that AE activity monitors rock deformation in geological boundaries due to convergence of the rock. In addition, high AE activity occurs in zones of dilatancy stress in homogenous rock. In conclusion in situ AE monitoring is a useful tool to observe instabilities in rock long before any damage becomes visible.

Location of acoustic emission (AE) events is one of the main evaluation tools in AE analysis. Reliable location of AE sources enables accurate investigation of the mechanisms that led to a crack in the material. It is known that the location errors are influenced by several factors, including the accuracy of the elastic wave arrival time reading, the geometric distribution of the AE sensors, and most importantly, by the physical properties of the propagation medium. The aim of this study is the application of a neural network to classify clustered AE events, which were detected during six hydraulic fracturing tests in massive salt rock. A fully connected feed forward network was used for pattern recognition and classification of the input events according to target classes. For input data the signal arrival time profiles of the longitudinal (L) and transversal (T) elastic waves were used to train, to test, and to validate the neural network. In total 765 AE events were classified in various target classes. Receiver operating characteristic analysis (ROC) was applied for analyzing the result of the neural network approach. The neural network classified clustered events correctly, while few spatially scattered events outside the region of a cluster could not be matched to any cluster. Bootstrap analysis showed that the results are robust and demonstrates the high potential of Deep Learning (DL) methods in the location of AE events.

To depict the pump power characteristics of radial centrifugal pumps, a physical model was developed. The model relies on established empirical equations. To parameterize the model for specific pumps, physically interpretable tuning factors were integrated. The tuning factors are identified by using the Levenberg–Marquardt method, which was applied to the characteristic curve at a constant speed. A cross-validation of the physical model highlighted the advantage of representing the set of performance curves with less deviation compared to approximation functions. Calculating the entire set of performance curves requires only one pump characteristic curve at a constant speed. Therefore, only a single measurement is necessary. Furthermore, the physical model can be used to calculate the changes in the set of performance curves due to prewhirl. This increases the accuracy of flow rate calculations when prewhirl occurs.

Mechanical seals ensure the internal sealing of centrifugal pumps from the surrounding environment. They are one of the most critical components in a centrifugal pump. For this reason, the condition of mechanical seals should be monitored during operation. Mechanical seal friction power is an important component of mechanical losses in centrifugal pumps and is used as an indicator of wear and therefore seal condition. The soft sensor described in this paper is based on temperature measurements at the seal and can be used for determining the frictional power performance. A major factor in determining frictional power performance is the heat transfer between the mechanical seal and the medium inside the pump. For calculating the heat transfer, the stationary temperature fields in the rings of the mechanical seal are described by transmission efficiencies. The root mean squared error was determined for steady-state operating conditions to assess the quality of the soft sensor calculation. The frictional power performance can be determined by recording the temperature at the mechanical seal mating ring and the medium. The algorithm detects when the steady-state operating conditions change but does not map the dynamic changes between the stationary operating conditions.