Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Matrix Factorization Techniques for Recommender Systems

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

A dual-LSTM framework combining change point detection and remaining useful life prediction

Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs.

Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder.

Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature.

Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis

Operating units, in practice, often suffer from multiple modes of failure, and each failure mode has a distinct influence on the service life cycle path of a unit. The rapid development of sensor and communication technologies has enabled multiple sensors to simultaneously monitor and track the health status of a unit in real time. However, one challenging question that remains to be resolved is how to leverage data from multiple sensors for better degradation modeling and prognostic analysis, especially when there are multiple failure modes. Currently, many of the existing approaches in prognostics either (a) fail to capture the dependency between sensors and instead focus on analyzing each sensor independently or (b) fail to incorporate the failure-mode diagnosis for better degradation modeling and prognostics during condition monitoring. To address the limitations in the existing literature, we propose a data-level fusion methodology to construct a composite failure-mode index, named FM-INDEX, ...

A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes

The rapid development of sensor and computing technology has created an unprecedented opportunity for condition monitoring and prognostic analysis in various manufacturing and healthcare industries. With the massive amount of sensor information available, important research efforts have been made in modeling the degradation signals of a unit and estimating its remaining useful life distribution. In particular, a unit is often considered to have failed when its degradation signal crosses a predefined failure threshold, which is assumed to be known a priori . Unfortunately, such a simplified assumption may not be valid in many applications given the stochastic nature of the underlying degradation mechanism. While there are some extended studies considering the variability in the estimated failure threshold via data-driven approaches, they focus on the failure threshold distribution of the population instead of that of an individual unit. Currently, the existing literature still lacks an effective approach to accurately estimate the failure threshold distribution of an operating unit based on its in-situ sensory data during condition monitoring. To fill this literature gap, this paper develops a convex quadratic formulation that combines the information from the degradation profiles of historical units and the in-situ sensory data from an operating unit to online estimate the failure threshold of this particular unit in the field. With a more accurate estimation of the failure threshold of the operating unit in real time, a better remaining useful life prediction is expected. Simulations as well as a case study involving a degradation dataset of aircraft turbine engines were used to numerically evaluate and compare the performance of the proposed methodology with the existing literature in the context of failure threshold estimation and remaining useful life prediction.

Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction

Due to the rapid development of sensing technologies, multiple sensors became available for real-time monitoring of the degradation status of machine systems. With such a wealth of data collected from multiple sensors, researchers have proposed different sensor fusion approaches for prognosis and condition monitoring, thus allowing accurate inference of the remaining useful life (RUL) of machine systems. However, almost no method provides a statistical metric to evaluate the quality of degradation signals for prognosis and condition monitoring. To fill this literature gap, this paper develops a sensor fusion framework to check the reliability of given degradation signals for prognosis and degradation analysis through a series of statistical hypothesis tests. A health index is constructed in the sensor fusion framework to help differentiate between distinct degradation states. Based on the health index, the remaining times to various degradation states are estimated, including the RUL to failure. A published degradation data set of aircraft engines is used to evaluate and compare the prognostic and condition monitoring performance of the proposed method with benchmark methods. Note to Practitioners —This paper aims to real-time forecast the condition of operating units given their real-time multisensor data. In particular, this paper proposes a data fusion framework that fuses the multisensor data to construct a health index signal. The health index is designed to distinguish between any two degradation states during the lifecycle of a unit. Therefore, it can be used to estimate the remaining life to reach any degradation state including failure, which helps to raise early maintenance and safety alarms. The overall procedure can be summarized in the following steps: 1) the architecture of the health index is learned from the multisensor data of historical units that already failed; 2) this architecture is then applied to the real-time multisensor data of an operating unit to construct its real-time health index signal; 3) the real-time health index signal is forecast for future time instances; and 4) for each forecast time instance, the forecast health index value is compared with the historical health index signals to find the most likely degradation state of the operating unit at that time instance. The proposed approach was shown to be effective for applications with a single failure mode.

Abdallah Chehade

Papers

A dual-LSTM framework combining change point detection and remaining useful life prediction

Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis

A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes

Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction

Sensor Fusion via Statistical Hypothesis Testing for Prognosis and Degradation Analysis