Deep Learning with Dynamically Weighted Loss Function for SensorBased Prognostics and Health Management Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the modelxe2x80x99s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the systemxe2x80x99s prognostics and diagnostics without modifying the modelsxe2x80x99 architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, onedimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long shortterm memory on the commercial modular aeropropulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamicallyweighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over nonweighted loss function predictions.

Analysis of the applicability of fault detection and failure prediction based on unsupervised learning and Monte Carlo simulations for real devices in the industrial automobile production Machine learning and statisticalbased methods have been widely used to estimate the Remaining Useful Life of devices. However, in most of the cases, the transferability of these methods to production environments is complicated. The majority of these algorithms have been tested by using simulated data, or by the construction of testbeds. The complication is generated by some real conditions in such environments which are not considered during the algorithm development. This research addresses those drawbacks by considering real data delivered by a production device in order to construct the right condition monitoring parameter. This parameter will be obtained by analyzing the correlation among available data, and failure on the critical element on the machine. Moreover, the benefits and drawbacks of several datadriven approaches are analyzed, and a method is proposed based on the combination of the best properties of each one.