Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

Wireless Measurement System for Structural Health Monitoring With High Time-Synchronization Accuracy

Abstract: Structural health monitoring (SHM) systems have excellent potential to improve the regular operation and maintenance of structures. Wireless networks (WNs) have been used to avoid the high cost of traditional generic wired systems. The most important limitation of SHM wireless systems is time-synchronization accuracy, scalability, and reliability. A complete wireless system for structural identification under environmental load is designed, implemented, deployed, and tested on three different real bridges. Our contribution ranges from the hardware to the graphical front end. System goal is to avoid the main limitations of WNs for SHM particularly in regard to reliability, scalability, and synchronization. We reduce spatial jitter to 125 ns, far below the 120 μs required for high-precision acquisition systems and much better than the 10-μs current solutions, without adding complexity. The system is scalable to a large number of nodes to allow for dense sensor coverage of real-world structures, only limited by a compromise between measurement length and mandatory time to obtain the final result. The system addresses a myriad of problems encountered in a real deployment under difficult conditions, rather than a simulation or laboratory test bed.

A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics

Abstract: In this paper, a temporal probabilistic approach based on the hidden Markov model (HMM), named physically segmented HMM with continuous output, is introduced for continuous tool condition monitoring in machinery systems. The proposed approach has the advantage of providing an explicit relationship between the actual health states and the hidden state values. The provided relationship is further exploited for formulation and parameter estimation in the proposed approach. The introduced approach is tested for continuous tool wear prediction in a computer numerical control milling machine and compared with two well-established neural network (NN) approaches, namely, multilayer perceptron and Elman network. In the experimental study, the prediction results are provided and compared after adopting appropriate hyper-parameter values for all the approaches by cross-validation. Based on the experimental results, physically segmented HMM approach outperforms the NN approaches. Moreover, the prognosis ability of the proposed approach is studied.

Efficient reactive monitoring

Abstract: Networks are monitored in order to ensure that the system operates within desirable parameters. The increasing complexity of networks and services provided by them increases this need for monitoring. Monitoring consists of measuring properties of the network, and of inferring an aggregate predicate from these measurements. Conducting such monitoring introduces traffic overhead that may reduce the overall effective throughput. This paper studies ways to minimize the monitoring communication overhead in IP networks. We develop and analyze several monitoring algorithms that achieve significant reduction in the management overhead while maintaining the functionality. The main idea is to combine global polling with local event driven reporting. The amount of traffic saving depends on the statistical characterization of the monitored data. We indicate the specific statistical factors that affect the saving and show how to choose the right algorithm for the type of monitored data. In particular our results show that for Internet traffic our algorithms can save more than 90% of the monitoring traffic.