Abstract:Failure prognostics is the process of predicting the remaining useful life (RUL) of machine components, which is vital for the predictive maintenance of industrial machinery. This paper presents a new deep learning approach for failure prognostics of rolling element bearings based on a Long Short-Term Memory (LSTM) predictor trained simultaneously within a Generative Adversarial Network (GAN) architecture. The LSTM predictor takes the current and past observations of a well-defined health index as an input, us… Show more
“…Several hybrid methods use different DL methods within the generator or discriminator of a GAN. Examples include using AEs [207], Adversarial AEs [105], AE-based DBN [136], Deep Convolutional GANs [227] or its conditional variant [108], LSTM [107], or BiLSTM and AEs [96,104] within a GAN.…”
<ul>
<li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br>
(RUL)<br>
</li>
<li>It discusses challenges and open problems faced in PdM<br>
</li>
<li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br>
</li>
<li>We propose the future direction for each challenge discussed in this article</li>
</ul>
“…Several hybrid methods use different DL methods within the generator or discriminator of a GAN. Examples include using AEs [207], Adversarial AEs [105], AE-based DBN [136], Deep Convolutional GANs [227] or its conditional variant [108], LSTM [107], or BiLSTM and AEs [96,104] within a GAN.…”
<ul>
<li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br>
(RUL)<br>
</li>
<li>It discusses challenges and open problems faced in PdM<br>
</li>
<li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br>
</li>
<li>We propose the future direction for each challenge discussed in this article</li>
</ul>
“…Several hybrid methods use different DL methods within the generator or discriminator of a GAN. Examples include using AEs [223], Adversarial AEs [120], AE-based DBN [151], Deep Convolutional GANs [246] or its conditional variant [123], LSTM [122], or BiLSTM and AEs [111,119] within a GAN.…”
Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-theart data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.
“…In the end, the authors demonstrated models trained with a mixture of real and realistic synthetic data exhibiting stronger prediction power in RUL estimation tasks for turbofan engines and lithium-ion battery systems. Recently, Lu et al proposed a jointly trainable GAN-LSTM predictor that significantly reduced RUL estimation errors for rolling element bearings [33].…”
<p>Amid the current digital transformation wave, predictive maintenance (PdM) using machine learning has become prevalent due to its potential to reduction for total cost and turnaround time in the manufacturing and MRO (maintenance, repair, and operations) sectors. Predicting machinery tool wear or remaining useful life is one of the essential applications in modern PdM. As a result, many deep learning based prediction methods emerged in recent years’ literature and continuously advancing the state-of-the-art. In this paper, we propose a tool wear prediction framework that orchestrates a range of machine learning techniques to enhance prediction accuracy. These techniques include sequence modeling with convolutional neural networks, synthetic data generation with conditional generative adversarial networks, and customized regularization strategy. Experiments were performed with two public milling datasets to evaluate the effectiveness of the proposed framework. Results show that our prediction model, sCNN-Ex, outperforms state-of-the-art tool wear prediction methods.</p>
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