An exTS based Neuro-Fuzzy algorithm for prognostics and tool condition monitoring.

International audienceThe growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving eXtended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed machining conditions. The performance of evolving Neuro-Fuzzy model is compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Multiple Regression Model (MRM) in term of accuracy and reliability through a case study of tool condition monitoring. The reliability of exTS also investigated

PID CONTROL : TUNING AND ROBUSTNESS

Ph.DDOCTOR OF PHILOSOPH

Adaptive Suboptimal Output-Feedback Control for Linear Systems using Integral Reinforcement Learning

Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft

Data-Driven Optimal Control with Reduced Output Measurements

This paper uses the integral reinforcement learning (IRL) technique to develop an online learning algorithm for finding suboptimal static output-feedback controllers for partially-unknown continuous-time (CT) linear systems. To our knowledge, this is the first static output-feedback control design method based on reinforcement learning for CT systems. In the proposed method, an online policy iteration (PI) algorithm is developed which uses the integral reinforcement knowledge for learning a suboptimal static output-feedback solution without requiring the drift knowledge of the system dynamics. Specifically, in the policy evaluation step of the PI algorithm, an IRL Bellman equation is used to evaluate an output-feedback policy, and in the policy improvement step of the PI algorithm the output-feedback gain is updated using the information given by the evaluated policy. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulations

Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes

In metal cutting processes, tool condition monitoring (TCM) plays an important role in maintaining the quality of surface finishing. Monitoring of tool wear in order to prevent surface damage is one of the difficult tasks in the context of TCM. Through early detection, high quality surface finishing and near-zero loss for potential failures can be ensured. Real-time/online tool degradation detection by using machine learning is highly desired. The ability to predict the tool wear, which is related to the remaining useful life of a tool, will improve efficiency and optimize tool usage while ensuring the quality of the work piece produced. In this paper, examine two popular methods of machine learning, namely the Adaptive Network Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) are used to estimate the tool wear and correlation models for tool wear estimation using ANFIS and SVM are estimated. A case study for six sets of ball nose cutters in a high speed milling machining process of Inconel 718 is carried out. Comparative studies of the two methods are carried out and experimental results analysed and discussed. In turns out that the accuracy of the ANFIS is generally better than the SVM whereas SVM is much faster than ANFIS in terms of speed

Application of classical clustering methods for online tool condition monitoring in high speed milling processes

Tool Condition Monitoring (TCM) is a necessary action during end-milling process as worn milling-tool might irreversibly damage the work-piece. So, there is an urgent need for a TCM system to provide an evaluation of the tool-wear progress and resulted surface roughness. Principally, in-process tool-wear and surface roughness measurements requires costly stopping of the milling machine. However, to implement the condition monitoring system, resulted signals of milling process are utilized to form a reference model that detects the performance of the system non-intrusively. Therefore, the needed milling-process reference model have to apply more beneficial feature extraction and AI techniques. Since the signals are continuous, their time-frequency analysis are applied for feature extraction. Also, proper AI-based modeling techniques have to be joined together to form a repeatable and generalizable reference model. As one of the available AI techniques that can make an insightful change in traditional AI based modeling techniques for the process, clustering methods are applied on the wavelet features of milling signal as an interpretation layer between the sensor signals and the next artificial intelligent blocks. This paper illustrates the consistency and repeatability of different clustering methods on wavelet features of force and vibration signal as well as a comparison in accordance to their performance and possible generalization for online condition monitoring and sequential clustering. Finally, fuzzy C-means clustering method is shown to be a useful AI-based block for a noise-robust and generalizable ball-nose milling reference model while it provides suitable platform for further investigations regarding online fault diagnosis and prognosis and sequential clustering