Data-driven Models for Remaining Useful Life Estimation of Aircraft Engines and Hard Disk Drives

Failure of physical devices can cause inconvenience, loss of money, and sometimes even deaths. To improve the reliability of these devices, we need to know the remaining useful life (RUL) of a device at a given point in time. Data-driven approaches use data from a physical device to build a model that can estimate the RUL. They have shown great performance and are often simpler than traditional model-based approaches. Typical statistical and machine learning approaches are often not suited for sequential data prediction. Recurrent Neural Networks are designed to work with sequential data but suffer from the vanishing gradient problem over time. Therefore, I explore the use of Long Short-Term Memory (LSTM) networks for RUL prediction. I perform two experiments. First, I train bidirectional LSTM networks on the Backblaze hard-disk drive dataset. I achieve an accuracy of 96.4\% on a 60 day time window, state-of-the-art performance. Additionally, I use a unique standardization method that standardizes each hard drive instance independently and explore the benefits and downsides of this approach. Finally, I train LSTM models on the NASA N-CMAPSS dataset to predict aircraft engine remaining useful life. I train models on each of the eight sub-datasets, achieving a RMSE of 6.304 on one of the sub-datasets, the second-best in the current literature. I also compare an LSTM network\u27s performance to the performance of a Random Forest and Temporal Convolutional Neural Network model, demonstrating the LSTM network\u27s superior performance. I find that LSTM networks are capable predictors for device remaining useful life and show a thorough model development process that can be reproduced to develop LSTM models for various RUL prediction tasks. These models will be able to improve the reliability of devices such as aircraft engines and hard-disk drives

Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks

In modern data centers, storage system failures are major contributors to downtimes and maintenance costs. Predicting these failures by collecting measurements from disks and analyzing them with machine learning techniques can effectively reduce their impact, enabling timely maintenance. While there is a vast literature on this subject, most approaches attempt to predict hard disk failures using either classic machine learning solutions, such as Random Forests (RFs) or deep Recurrent Neural Networks (RNNs). In this work, we address hard disk failure prediction using Temporal Convolutional Networks (TCNs), a novel type of deep neural network for time series analysis. Using a real-world dataset, we show that TCNs outperform both RFs and RNNs. Specifically, we can improve the Fault Detection Rate (FDR) of ≈ 7.5% (FDR = 89.1%) compared to the state-of-the-art, while simultaneously reducing the False Alarm Rate (FAR = 0.052%). Moreover, we explore the network architecture design space showing that TCNs are consistently superior to RNNs for a given model size and complexity and that even relatively small TCNs can reach satisfactory performance. All the codes to reproduce the results presented in this paper are available at https://github.com/ABurrello/tcn-hard-disk-failure-prediction

Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters

On a daily basis, data centers process huge volumes of data backed by the proliferation of inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage providers perform condition-based monitoring and replace hard disks before they fail. By estimating the remaining useful life of hard disk drives, one can predict the time-to-failure of a particular device and replace it at the right time, ensuring maximum utilization whilst reducing operational costs. In this work, large-scale predictive analyses are performed using severely skewed health statistics data by incorporating customized feature engineering and a suite of sequence learners. Past work suggests using LSTMs as an excellent approach to predicting remaining useful life. To this end, we present an encoder-decoder LSTM model where the context gained from understanding health statistics sequences aid in predicting an output sequence of the number of days remaining before a disk potentially fails. The models developed in this work are trained and tested across an exhaustive set of all of the 10 years of S.M.A.R.T. health data in circulation from Backblaze and on a wide variety of disk instances. It closes the knowledge gap on what full-scale training achieves on thousands of devices and advances the state-of-the-art by providing tangible metrics for evaluation and generalization for practitioners looking to extend their workflow to all years of health data in circulation across disk manufacturers. The encoder-decoder LSTM posted an RMSE of 0.83 during training and 0.86 during testing over the exhaustive 10 year data while being able to generalize competitively over other drives from the Seagate family.Comment: 8 pages, 9 figures and 6 table

Model-Augmented Estimation of Conditional Mutual Information for Feature Selection

Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independence (CI) testing, which tend to struggle either with the curse of dimensionality or computational complexity. We propose a novel two-step approach which facilitates Markov blanket feature selection in high dimensions. First, neural networks are used to map features to low-dimensional representations. In the second step, CI testing is performed by applying the $k$-NN conditional mutual information estimator to the learned feature maps. The mappings are designed to ensure that mapped samples both preserve information and share similar information about the target variable if and only if they are close in Euclidean distance. We show that these properties boost the performance of the $k$-NN estimator in the second step. The performance of the proposed method is evaluated on both synthetic and real data.Comment: Accepted to UAI 202

Modeling repairable system failure data using NHPP realiability growth mode.

Stochastic point processes have been widely used to describe the behaviour of repairable systems. The Crow nonhomogeneous Poisson process (NHPP) often known as the Power Law model is regarded as one of the best models for repairable systems. The goodness-of-fit test rejects the intensity function of the power law model, and so the log-linear model was fitted and tested for goodness-of-fit. The Weibull Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the WTTE-RNN framework is only appropriate failure data with independent and identically distributed interarrival times of successive failures, and so cannot be applied to nonhomogeneous Poisson process

Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer

Cloud services are omnipresent and critical cloud service failure is a fact of life. In order to retain customers and prevent revenue loss, it is important to provide high reliability guarantees for these services. One way to do this is by predicting outages in advance, which can help in reducing the severity as well as time to recovery. It is difficult to forecast critical failures due to the rarity of these events. Moreover, critical failures are ill-defined in terms of observable data. Our proposed method, Outage-Watch, defines critical service outages as deteriorations in the Quality of Service (QoS) captured by a set of metrics. Outage-Watch detects such outages in advance by using current system state to predict whether the QoS metrics will cross a threshold and initiate an extreme event. A mixture of Gaussian is used to model the distribution of the QoS metrics for flexibility and an extreme event regularizer helps in improving learning in tail of the distribution. An outage is predicted if the probability of any one of the QoS metrics crossing threshold changes significantly. Our evaluation on a real-world SaaS company dataset shows that Outage-Watch significantly outperforms traditional methods with an average AUC of 0.98. Additionally, Outage-Watch detects all the outages exhibiting a change in service metrics and reduces the Mean Time To Detection (MTTD) of outages by up to 88% when deployed in an enterprise cloud-service system, demonstrating efficacy of our proposed method.Comment: Accepted to ESEC/FSE 202