Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad

A Renewable and Ultrasensitive Electrochemiluminescence Immunosenor Based on Magnetic RuL@SiO2-Au∼RuL-Ab2 Sandwich-Type Nano-Immunocomplexes

An ultrasensitive and renewable electrochemiluminescence (ECL) immunosensor was developed for the detection of tumor markers by combining a newly designed trace tag and streptavidin-coated magnetic particles (SCMPs). The trace tag (RuL@SiO2-Au∼RuL-Ab2) was prepared by loading Ru(bpy)32+(RuL)-conjuged secondary antibodies (RuL-Ab2) on RuL@SiO2 (RuL-doped SiO2) doped Au (RuL@SiO2-Au). To fabricate the immunosensor, SCMPs were mixed with biotinylated AFP primary antibody (Biotin-Ab1), AFP, and RuL@SiO2-Au∼RuL-Ab2 complexes, then the resulting SCMP/Biotin-Ab1/AFP/RuL@SiO2-Au∼RuL-Ab2 (SBAR) sandwich-type immunocomplexes were absorbed on screen printed carbon electrode (SPCE) for detection. The immunocomplexes can be easily washed away from the surface of the SPCE when the magnetic field was removed, which made the immunosensor reusable. The present immunosensor showed a wide linear range of 0.05–100 ng mL−1 for detecting AFP, with a low detection limit of 0.02 ng mL−1 (defined as S/N = 3). The method takes advantage of three properties of the immunosensor: firstly, the RuL@SiO2-Au∼RuL-Ab2 composite exhibited dual amplification since SiO2 could load large amount of reporter molecules (RuL) for signal amplification. Gold particles could provide a large active surface to load more reporter molecules (RuL-Ab2). Accordingly, through the ECL response of RuL and tripropylamine (TPA), a strong ECL signal was obtained and an amplification analysis of protein interaction was achieved. Secondly, the sensor is renewable because the sandwich-type immunocomplexes can be readily absorbed or removed on the SPCE’s surface in a magnetic field. Thirdly, the SCMP modified probes can perform the rapid separation and purification of signal antibodies in a magnetic field. Thus, the present immunosensor can simultaneously realize separation, enrichment and determination. It showed potential application for the detection of AFP in human sera

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

Can we apply accelerator-cores to control-intensive programs?

There is a trend towards using accelerators to increase performance and energy efficiency of general-purpose processors. So far, most accelerators have been build with HPC-applications in mind. A question that arises is how well can other applications benefit from these accelerators? In this paper, we discuss the acceleration of three benchmarks using the SPUs of a Cell-BE. We analyze the potential speedup given the inherent parallelism in the applications. While the potential speedup is significant in all benchmarks, the obtained speedup lags behind due to a mismatch between micro-architectural properties of the accelerators and the benchmark properties

Factoring out ordered sections to expose thread-level parallelism

With the rise of multi-core processors, researchers are taking a new look at extending the applicability auto-parallelization techniques. In this paper, we identify a dependence pattern on which autoparallelization currently fails. This dependence pattern occurs for ordered sections, i.e. code fragments in a loop that must be executed atomically and in original program order. We discuss why these ordered sections prohibit current auto-parallelizers from working and we present a technique to deal with them. We experimentally demonstrate the efficacy of the technique, yielding significant overall program speedups

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions