Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

A topical review of numerical and experimental studies of supercontinuum generation in photonic crystal fiber is presented over the full range of experimentally reported parameters, from the femtosecond to the continuous-wave regime. Results from numerical simulations are used to discuss the temporal and spectral characteristics of the supercontinuum, and to interpret the physics of the underlying spectral broadening processes. Particular attention is given to the case of supercontinuum generation seeded by femtosecond pulses in the anomalous group velocity dispersion regime of photonic crystal fiber, where the processes of soliton fission, stimulated Raman scattering, and dispersive wave generation are reviewed in detail. The corresponding intensity and phase stability properties of the supercontinuum spectra generated under different conditions are also discussed.

Supercontinuum generation, photonic crystal fiber

http://www2.mae.ufl.edu/nkim/Papers/paper70.pdf

Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

Prognostics focuses on predicting the future performance of a system, specifically the time at which the system no long performs its desired functionality, its time to failure. As an important aspect of prognostics, remaining useful life (RUL) prediction estimates the remaining usable life of a system, which is essential for maintenance decision making and contingency mitigation. A significant amount of research has been reported in the literature to develop prognostics models that are able to predict a system's RUL. These models can be broadly categorized into experience-based models, date-driven models, and physics-based models. However, due to system complexity, data availability, and application constraints, there is no universally accepted best model to estimate RUL. The review part of this paper specifically focused on the development of hybrid prognostics approaches, attempting to leverage the advantages of combining the prognostics models in the aforementioned different categories for RUL prediction. The hybrid approaches reported in the literature were systematically classified by the combination and interfaces of various types of prognostics models. In the case study part, a hybrid prognostics method was proposed and applied to a battery degradation case to show the potential benefit of the hybrid prognostics approach.

Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

In attosecond and strong-field physics, the acquisition of data in an acceptable time demands the combination of high peak power with high average power. We report a 21 W mid-IR optical parametric chirped pulse amplifier (OPCPA) that generates 131 μJ and 97 fs (sub-9-cycle) pulses at a 160 kHz repetition rate and at a center wavelength of 3.25 μm. Pulse-to-pulse stability of the carrier envelope phase (CEP)-stable output is excellent with a 0.33% rms over 288 million pulses (30 min) and compression close to a single optical cycle was achieved through soliton self-compression inside a gas-filled mid-IR antiresonant-guiding photonic crystal fiber. Without any additional compression device, stable generation of 14.5 fs (1.35-optical-cycle) pulses was achieved at an average power of 9.6 W. The resulting peak power of 3.9 GW in combination with the near-single-cycle duration and intrinsic CEP stability makes our OPCPA a key-enabling technology for the next generation of extreme photonics, strong-field attosecond research, and coherent x-ray science.

High average power and single-cycle pulses from a mid-IR optical parametric chirped pulse amplifier

Graphical abstractDisplay Omitted HighlightsA hybrid/fusion prognostics framework to predict remaining useful life by combining the data-driven methods and model-based methods.Introduce a data-driven method to estimate the measurement model in a model-based particle filter framework.Introduce a data-driven method to predicted future measurement in long term prediction in a model-based particle filter framework.Shown improved prediction accuracy using battery as a case study. Remaining useful life prediction is one of the key requirements in prognostics and health management. While a system or component exhibits degradation during its life cycle, there are various methods to predict its future performance and assess the time frame until it does no longer perform its desired functionality. The proposed data-driven and model-based hybrid/fusion prognostics framework interfaces a classical Bayesian model-based prognostics approach, namely particle filter, with two data-driven methods in purpose of improving the prediction accuracy. The first data-driven method establishes the measurement model (inferring the measurements from the internal system state) to account for situations where the internal system state is not accessible through direct measurements. The second data-driven method extrapolates the measurements beyond the range of actually available measurements to feed them back to the model-based method which further updates the particles and their weights during the long-term prediction phase. By leveraging the strengths of the data-driven and model-based methods, the proposed fusion prognostics framework can bridge the gap between data-driven prognostics and model-based prognostics when both abundant historical data and knowledge of the physical degradation process are available. The proposed framework was successfully applied on lithium-ion battery remaining useful life prediction and achieved a significantly better accuracy compared to the classical particle filter approach.

A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction

Although ultraviolet (UV) light is important in many areas of science and technology, there are very few if any lasers capable of delivering wavelength-tunable ultrashort UV pulses at high repetition rates. Here we report the generation of deep UV laser pulses at megahertz repetition rates and microjoule energies by means of dispersive wave (DW) emission from self-compressed solitons in gas-filled single-ring hollow-core photonic crystal fiber (SR-PCF). Pulses from an ytterbium fiber laser (∼300  fs) are first compressed to <25  fs in a SR-PCF-based nonlinear compression stage and subsequently used to pump a second SR-PCF stage for broadband DW generation in the deep UV. The UV wavelength is tunable by selecting the gas species and the pressure. Through rigorous optimization of the system, in particular employing a large-core fiber filled with light noble gases, we achieve 1 μJ pulse energies in the deep UV, which is more than 10 times higher, at average powers more than four orders of magnitude greater (reaching 1 W) than previously demonstrated, with only 20 μJ pulses from the pump laser.

Generation of microjoule pulses in the deep ultraviolet at megahertz repetition rates

Spectral anti-crossings between the fundamental guided mode and core-wall resonances alter the dispersion in hollow-core anti-resonant-reflection photonic crystal fibers. Here we study the effect of this dispersion change on the nonlinear propagation and dynamics of ultrashort pulses. We find that it causes emission of narrow spectral peaks through a combination of four-wave mixing and dispersive wave emission. We further investigate the influence of the anti-crossings on nonlinear pulse propagation and show that their impact can be minimized by adjusting the core-wall thickness in such a way that the anti-crossings lie spectrally distant from the pump wavelength.

Felix Köttig

Papers

Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

High average power and single-cycle pulses from a mid-IR optical parametric chirped pulse amplifier

A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction

Generation of microjoule pulses in the deep ultraviolet at megahertz repetition rates

Effect of anti-crossings with cladding resonances on ultrafast nonlinear dynamics in gas-filled photonic crystal fibers