Recurrent neural networks for remaining useful life estimation

Heimes, F.

doi:10.1109/phm.2008.4711422

Cited by 475 publications

(304 citation statements)

References 2 publications

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“…This implies that the data from the operational variables is relatively 'simple' compared to the data from the non-operational variables, since the intrinsic cardinalities and reduced description lengths of the data from the operational variables are relatively small. This result was confirmed by a Prognostics expert: the hypothesis for filtering out the operational variables is that data from operational variables tends to have simpler behavior, since there are only several crucial states for the engines (Heimes and BAE Systems 2008;PHM Data Challenge Competition 2008; Prognostics Center of Excellence, National Aeronautics and Space Administration (NASA) 2012; Wang et al 2008;Wang and Lee 2006). Note that in our experiment we did not need to tune any parameters, while most of the related literature for this dataset use multilayer perceptron neural networks (Heimes and BAE Systems 2008;Wang et al 2008;Wang and Lee 2006), which have the overhead of parameter tuning and are prone to overfitting.…”

Section: An Example Application In Prognosticsmentioning

confidence: 68%

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Rakthanmanon

Hao

et al. 2014

Data Min Knowl Disc

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Many algorithms for data mining or indexing time series data do not operate directly on the raw data, but instead they use alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this work, we investigate the problem of discovering the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to automatically discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important subroutine in algorithms for classification, clustering and outlier discovery. We will frame 123Discover the intrinsic cardinality and dimensionality of time series 359 the discovery of these intrinsic features in the Minimal Description Length framework. Extensive empirical tests show that our method is simpler, more general and more accurate than previous methods, and has the important advantage of being essentially parameter-free.

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Section: An Example Application In Prognosticsmentioning

confidence: 68%

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Rakthanmanon

Hao

et al. 2014

Data Min Knowl Disc

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“…And to make further comparisons with neural networks based (Heimes 2008) and similarity based (Wang et al 2008) algorithms, we compare our methodology with stateof-the-art methods of these two algorithms that have nice performances at PHM'08 data challenge competition. The result is shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Machine prognostics based on sparse representation model

Ren

Jiang

2015

J Intell Manuf

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The prognostic technologies for machines refer to the estimation of machines' remaining useful life using monitoring data from sensors. Different from traditional maintenance strategies, this maintenance strategy can reduce downtime, maintenance costs and critical risks. Given these advantages, an increasing number of prognostic models are introduced. Data driven methods such as neural networks and Bayesian approaches are used widely in machine prognostics. However, the sequential information and inherent relationships among historical data are rarely considered in these models. So, the estimations are usually not accurate enough. In our paper, we take a novel methodology to estimate the remaining useful life: first, we adopt sparse representation model to extract the inherent relationships of training samples and measure the similarities between testing samples and training samples, and then a weight is given to every training sample to note its similarity to the testing sample. When all testing samples are measured, a hierarchical Hough voting process utilizing the sequential information of monitoring data is carried out to evaluate the remaining useful life. The industry experiment has proven the effectiveness of our approach.

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“…It was a pure data-driven problem. Heimes [41] used recurrent neural network trained by extended Kalman filter to solve this problem. Wang, Yu, Siegel, and Lee [42] used similarity-based prognostics to tackle the PHM08 problem.…”

Section: Data-driven Prognosticsmentioning

confidence: 99%

Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

143

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Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

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Recurrent neural networks for remaining useful life estimation

Cited by 475 publications

References 2 publications

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Machine prognostics based on sparse representation model

Prognostics: a literature review

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