Analyse spectrale singulière des signaux vibratoires et Machine Learning pour la surveillance d'usure d'outils

Cette étude explore l'utilisation des techniques de Machine Learning pour la classification de l'état d'outils en usinage. Une analyse spectrale singulière (ASS) pseudo-locale des signaux vibratoires relevés sur le porte-outil, couplée à un filtrage passe-bande a permis la définition et la mise en évidence d'indicateurs très sensibles à l'évolution de l'état de l'outil. Ces indicateurs sont définis à partir des sommes des raies spectrales des signaux reconstruits par ASS et de leurs résidus, dans des gammes de fréquence judicieusement choisies. Les taux de reconnaissance de l'usure sont très bons et dépassent les 80 %. Cette étude met en évidence deux aspects importants : la forte richesse en information des composantes hautes fréquences des signaux vibratoires et la possibilité de s'affranchir du bruit inutile par la combinaison de l'ASS et d'un filtrage passe-bande

Evaluation of the performance of infrared thermography for on-line condition monitoring of rotating machines

peer reviewedThis study evaluated the possibility of infrared thermography to measure accurately the temperature of elements of a rotating device, within the scope of condition monitoring. The tested machine was a blower coupled to a 500 kW electric motor, that operated in multiples regimes. The thermograms were acquired by a fixed thermographic camera and were processed and recorded every 15 minutes. Because the normal temperature variations could easily mask a drift caused by a failure, a corrected temperature was computed using autorecursive models. It was shown that an efficient temperature correction should compensate for the variations of the process, and for the ambient temperatures variations, either daily or seasonal. The standard deviation of the corrected temperature was of a few tenth of degree, making possible the detection of a drift of less than one degree and the prediction of potential failure.FiaM

Modeling Impulsive Ball Mill Forces Effects on the Dynamic Behavior of a Single-Stage Gearbox

Gearboxes are frequently used in the mining industry, especially for power transmission between the electric drive and the ball mill; besides the extreme complexity of a ball mill gear transmission system, the fault diagnosis by vibration analysis can be easily distorted by the presence of impulsive noises due to the ball pulses on the mill shell. Although several works in the literature are related to the influence of an impulsive noise on the accuracy of the diagnosis, no dynamic model exists yet in the literature that can explain the influence of these forces on the dynamic behavior of gearboxes. This paper presents a new approach to determine the influence of the grinding forces in crack defects diagnosis. This approach is based on a hybrid numerical model of a 24-degree-of-freedom gearbox, simulating one gear train and two drive shafts. The impact forces of the mill drum are modelled by a discrete element method (DEM). The ball-filling rate (Fr), the mill speed (Nr), and the ball size (Db) are considered to study this phenomenon. The simulations results show by a time series representation, fast Fourier transform, and short-time Fourier transform (STFT), that the acceleration is significantly affected by the presence of the grinding forces, developing an impulsive noise due to the impact of the balls governed by the studied parameters

Condition monitoring of gear grinding processes

Grinding is one of the most prominent abrasive processes with geometrically non-defined cutting edges and is widely applied to achieve accuracy and high quality mechanical, electrical and optical parts, generating the final surface quality of machined parts. The grinding process dependents on the tool performance, on the machine stability as well as on the correct clamping/positioning of the workpiece. Monitoring systems of the grinding process could be capable of detecting any unexpected malfunctions in the process with high reliability leading to a minimization of substandard parts and maintaining the desired workpiece quality. Grinding is often used in gear manufacturing process as a last phase being the finishing operation which shapes the micro-geometry of the gear tooth flank and improves its surface quality. This step is decisive as it has a direct impact on the operating quality of gears and in particular on the running noise behavior of the end product. Among other procedures, online vibration monitoring could be used in order to a) evaluate the quality of the workpieces and detect defects that occurred in prior processes and b) detect and identify grinding process malfunctions at an early stage. The process monitoring could be used as a product quality control and could lead to the overall reduction of production losses and to the prevention of sending defective parts to customers. The goal of this paper is to propose a number of features which could be used in order to monitor the grinding process and identify specific type of defects. As significant material removal takes place and due to the kinematics of the gear grinding process (entering/exiting of the worm into the workpiece, and alternating number of contact points), the cutting forces vary leading to non-stationary (load varying) operating conditions. The methodology is evaluated on real signals captured during the emulation of process malfunctions of a gear grinding machine.status: publishe

Development of an e-Platform for Managing Maintenance Operations

Rapid development of ITC technologies and decreasing cost of their implications pave the road for the vast applications of condition monitoring in industrial maintenance. In the mean time, maintenance outsourcing of the crucial equipments, as a part of new business strategy, has become a common practice of the SMEs to pursuit their commercial objectives. Implementations of the condition-based maintenance polices with the combination of outsourcing practices create an advanced collaborative business mode in the maintenance service market. To maximize the benefits of employing the new technologies and to deal with the complexity in the new business processes, efficient coordination (internal & external) is essentially required so as to be able to treat timely the immense signals from the sensors, to share information between the actors, to analyze system’s reliability, to choose the suitable strategies & policies, to negotiate between partners, and to plan & execute the maintenance actions. In search for the optimal solution to meet the needs of such management complexity, a cyber platform was defined and is being developed. This paper analyzes the business needs of maintenance outsourcing in nowadays tech-economic environments and presents the framework structure, the main characteristics, the working mechanism, and the key advantages of such management platform that was elaborated on the base of the operational prototype of an outsourcing company. Deep impacts of the platform on the future business modes and maintenance operations will be analyzed, assessed, and prospected