The useful life time of equipment is an important variable related to system prognosis, and its accurate estimation leads to several competitive advantage in industry. In this paper, Remaining Useful Lifetime (RUL) prediction is estimated by Particle Swarm optimized Support Vector Machines (PSO+SVM) considering two possible pre-processing techniques to improve input quality: Empirical Mode Decomposition (EMD) and Wavelet Transforms (WT). Here, EMD and WT coupled with SVM are used to predict RUL of bearing from the IEEE PHM Challenge 2012 big dataset. Specifically, two cases were analyzed: considering the complete vibration dataset and considering truncated vibration dataset. Finally, predictions provided from models applying both pre-processing techniques are compared against results obtained from PSO+SVM without any pre-processing approach. As conclusion, EMD+SVM presented more accurate predictions and outperformed the other models

Knowledge on reliability and maintenance performance of physical assets can bring competitive advantages for companies (e.g., shorter downtimes and higher operation continuity). Prognostic and Health Management (PHM) can be used to improve not only the availability, but also the quality of the system. Furthermore, the assessment of important measuresin PHM, such as the Remaining Useful Life (RUL), facilitates the development of action plans such as maintenance programs or inventory acquisition. A well-known method that has been successfully used to estimate RUL is Support Vector Machines (SVM), which present the advantage that knowledge about the function's behavior and the relationship between input and output are not required. In addition, SVM solves a quadratic and convex optimization problem on the training set for which the Karush-Kuhn-Tucker (KKT) first order conditions assure a global maximum. However, SVM does not provide satisfactory results when dealing with non-stationary series with monotonic trend. In this context, Empirical Mode Decomposition (EMD) raises as an alternative to tackle this issue. In this paper, a EMD+SVM based approach is used for predicting the RUL based on non-stationary series, and is applied in two cases with different behaviors. The results indicate that EMD+SVM presents an improved performance in comparison to stand-alone SVM for both case studies.

Prognostics and Health Management (PHM) concerns predicting machines’ behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines’ state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models’ speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.