Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre-whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.
The analysis of vibrations from rotating machines gives information about their faults. From the signal processing perspective a significant problem is the detection of weak signals embedded in strong noise. Stochastic resonance (SR) is a mechanism where noise is not suppressed but exploited to trigger the synchronization of a non-linear system and in its one-dimensional form has been recently applied to vibration analysis. This paper focuses on the use of SR in a twodimensional system of gradient type for detection of weak signals submerged in Gaussian noise. Comparing the traditional one-dimensional system and the two-dimensional used here, this paper shows that the latter can offer a more sensitive means of detection. An alternative metric is proposed to assess the output signal quality, requiring no a priori knowledge of the signal to be detected, and it is shown to offer similar results to the more conventional signal-to-noise ratio.
Predictive maintenance strategies which estimate remaining useful life of system components to prevent breakdowns and down-times by timely and well-scheduled maintenance ensures the reliable availability of assets and lowers total costs of ownership. The focus on the components’ life times falls short, however, to infer the system-level capability to achieve upcoming tasks, especially if these tasks vary either in the strain they cause for the system or in the environmental conditions in which the system needs to perform. Such an assessment of the health and mission readiness of a system is crucial for mobile assets like seafaring vessels undertaking long-term operations without the option to easily come in for repairs or for industrial assets that need to complete long production runs in one go under varying circumstances. We propose a multi-step methodology to achieve such assessments using both Bayesian reasoning for diagnosis and prognosis and physics-based simulation models. First, we construct an appropriate Bayesian network in an object-oriented way by fitting a pre-compiled library of network fragments to the system’s schematics using generative techniques. We then parameterize the obtained network using a combination of expert knowledge and machine learning to fine tune system-level interactions between components and their link to the system’s performance. The learning step uses past operational data that we augment or complement with synthetic data, created by a physics-based simulation model, where needed. Finally, we use the trained Bayesian network to assess the mission readiness of the system given the probabilistics of its diagnosed state, expected impact of possible maintenance interventions, and the estimated profile of the future use. We illustrate and verify our methodology on a cooling system with an active feedback control loop, but our approach for mission readiness assessment is domain-independent, universally applicable, and typically feasible where operational data and engineering knowledge can be brought together to solve its challenge.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.