A Robust Method for Drilling Monitoring using Acoustic Emission

Acoustic Emission (AE) is considered an efficient tool for monitoring of machining operations, for both tool condition and working piece integrity. However, the use of AE is more challenging in case of drilling, due to heavy dependence of AE signals to process parameters. Monitoring drilling using AE thus requires robust methods to extract useful information in signals. The paper describes such a method that adapts itself to AE signals obtained during drilling, allowing the automatic set-up of an adaptive threshold to perform AE count rate. Experiments have been conducted that show the robustness of the method and its usefulness in drilling monitoring.International audienceAcoustic Emission (AE) is considered an efficient tool for monitoring of machining operations, for both tool condition and working piece integrity. However, the use of AE is more challenging in case of drilling, due to heavy dependence of AE signals to process parameters. Monitoring drilling using AE thus requires robust methods to extract useful information in signals. The paper describes such a method that adapts itself to AE signals obtained during drilling, allowing the automatic set-up of an adaptive threshold to perform AE count rate. Experiments have been conducted that show the robustness of the method and its usefulness in drilling monitoring

Multisensor data fusion and belief functions for robust singularity detection in signals

This paper addresses the problem of robust detection of signal singularity in hostile environments using multisensor data fusion. Measurement uncertainty is usually treated in a probabilistic way, assuming lack of knowledge is totally due to random effects. However, this approach fails when other effects, such as sensor failure, are involved. In order to improve the robustness of singularity detection, an evidence theory based approach is proposed for both modeling (data alignment) and merging (data fusion) information coming from multiple redundant sensors. Whereas the fusion step is done classically, the proposed method for data alignment has been designed to improve singularity detection performances in multisensor cases. Several case studies have been designed to suit real life situations. Results provided by both probabilistic and evidential approaches are compared. Evidential methods show better behavior facing sensors dysfunction and the proposed method takes fully advantage of component redundancy

Feature Selection for Complex Systems Monitoring: an Application using Data Fusion

Emergence of automated and flexible production means leads to the need of robust monitoring systems. Such systems are aimed at the estimation of the production process state by deriving it as a function of critical variables, called features, that characterize the process condition. The problem of feature selection, which consists, given an original set of features, in finding a subset such the estimation accuracy of the monitoring system is the highest possible, is therefore of major importance for sensor-based monitoring applications. Considering real-world applications, feature selection can be tricky due to imperfection on available data collections: depending on the data acquisition conditions and the monitored process operating conditions, they can be heterogeneous, incomplete, imprecise, contradictory, or erroneous. Classical feature selection techniques lack of solutions to deal with uncertain data coming from different collections. Data fusion provides solutions to process these data collections altogether in order to achieve coherent feature selection, even in difficult cases involving imperfect data. In this work, condition monitoring of the tool in industrial drilling systems will serve as a basis to demonstrate how data fusion techniques can be used to perform feature selection in such difficult cases

A Robust Method for Drilling Monitoring using Acoustic Emission

Acoustic Emission (AE) is considered an efficient tool for monitoring of machining operations, for both tool condition and working piece integrity. However, the use of AE is more challenging in case of drilling, due to heavy dependence of AE signals to process parameters. Monitoring drilling using AE thus requires robust methods to extract useful information in signals. The paper describes such a method that adapts itself to AE signals obtained during drilling, allowing the automatic set-up of an adaptive threshold to perform AE count rate. Experiments have been conducted that show the robustness of the method and its usefulness in drilling monitoring

Three-dimensional dynamic rupture simulations across interacting faults: The M w 7.0, 2010, Haiti earthquake

International audienceThe mechanisms controlling rupture propagation between fault segments during a large earthquake are key to the hazard posed by fault systems. Rupture initiation on a smaller fault sometimes transfers to a larger fault, resulting in a significant event (e.g., 2002 M7.9 Denali USA and 2010 M7.1 Darfield New Zealand earthquakes). In other cases rupture is constrained to the initial fault and does not transfer to nearby faults, resulting in events of more moderate magnitude. This was the case of the 1989 M6.9 Loma Prieta and 2010 M7.0 Haiti earthquakes which initiated on reverse faults abutting against a major strike-slip plate boundary fault but did not propagate onto it. Here we investigate the rupture dynamics of the Haiti earthquake, seeking to understand why rupture propagated across two segments of the Léogâne fault but did not propagate to the adjacent Enriquillo Plantain Garden Fault, the major 200 km long plate boundary fault cutting through southern Haiti. We use a finite element model to simulate propagation of rupture on the Léogâne fault, varying friction and background stress to determine the parameter set that best explains the observed earthquake sequence, in particular, the ground displacement. The two slip patches inferred from finite fault inversions are explained by the successive rupture of two fault segments oriented favorably with respect to the rupture propagation, while the geometry of the Enriquillo fault did not allow shear stress to reach failure

3-D velocity structure in southern Haiti from local earthquake tomography

International audienceWe investigate 3-D local earthquake tomography for high-quality travel time arrivals from aftershocks following the 2010 M7.0 Haiti earthquake on the Léogâne fault. The data were recorded by 35 stations, including 19 ocean bottom seismometers, from which we selected 595 events to simultaneously invert for hypocenter location and 3-D Vp and Vs velocity structures in southern Haiti. We performed several resolution tests and concluded that clear features can be recovered to a depth of 15 km. At 5 km depth we distinguish a broad low-velocity zone in the Vp and Vs structure offshore near Gonave Island, which correlate with layers of marine sediments. Results show a pronounced low-velocity zone in the upper 5 km across the city of Léogâne, which is consistent with the sedimentary basin location from geologic map. At 10 km depth, we detect a low-velocity anomaly offshore near the Trois Baies fault and a NW-SE directed low-velocity zone onshore across Petit-Goâve and Jacmel, which is consistent with a suspected fault from a previous study and that we refer to it in our study as the Petit-Goâve-Jacmel fault. These observations suggest that low-velocity structures delineate fault structures and the sedimentary basins across the southern peninsula, which is extremely useful for seismic hazard assessment in Haiti