This paper uses a linear transformer model to investigate the effect of the lift-off on the results of the thickness measurement of nonferromagnetic metallic plates. The transformer model previews that the time derivative of the magnetization curves obtained for different gaps between the excitation coil and the plate should intercept in a single point when low magnetic coupling factors are considered. To assess the validity of the model, results are compared with experimental data obtained with a giant magnetoresistive sensor probe. For comparison, the sensor output voltage time derivative must be performed as well. The similarity of the theoretical model results and those obtain experimentally with pulsed excitation, confirms the correctness of the transformer approach.

This paper introduces a novel framework for industrial wireless sensor networks (IWSNs) used for machine condition monitoring (MCM). Our approach enables the use of state-of-the-art computationally intensive classifiers in computationally weak sensor network nodes. The key idea is to split data acquisition, classifier building and training, and the operation phase, between different units. Computationally demanding processing is carried out in the central unit, while other tasks are distributed to the sensor nodes using over-the-air programming. The system is autonomously trained on the healthy state of a machine and then monitors a change in behavior which indicates a faulty state. Thanks to one-class classification, there is no need to introduce the faulty state of the machine in the training phase. We extend the diagnostic capability of the system using dynamic changes in the data acquisition and classification parts of the program in the sensor nodes. This enables the system to react to ambiguous machine states by temporarily changing the diagnostic focus. Compressing the information in the individual sensor nodes provided by in-node classification allows us to transmit only the classification result, instead of full signal waveforms. This enables the MCM system to be deployed with a large number of nodes, even with high sampling rates. The proposed concept was evaluated in IRIS IWSN by means of a rotary machine simulator.

The early alert monitoring system for an effective scheduled maintenance strategy based on a wireless technology requires reliable transfer of the diagnostic information between the sensor and the gateway. This paper presents an industrial wireless sensor network (IWSN)-based machine condition monitoring (MCM) system capable of overcoming a false indication caused by temporary loss of data, signal interference, or invalid data. We use multisensor fusion driven by a quality parameter, which is produced by each sensor node according to the data history outliers and the actual state of the node. The fusion node provides a quality evaluation on its output as well. This novel approach enables the propagation of information about the uncertainty of a measured value from the source node to the sink node. Thus, potential degradation of acquired or transferred diagnostic information is minimized. Instead of raw data, the signal features are transferred, so that bandwidth savings are rapidly improved. The proposed concept was experimentally verified on real wireless sensor network (WSN) hardware. The performance evaluated using the signal-to-noise ratio and false-alarm rate detection demonstrates the effectiveness of the proposed approach.