Abstract The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame.

In order to meet the increasing demand for more performance with reduced power consumption and chip form-factor, semiconductor manufacturing is moving toward 3-D stacked integrated circuits (SICs). One of the challenges in bringing this technology into realization is the complicated test accessibility requirements of 3-D chips, which apart from having adequate defect coverage, should also have minimal test time. A major limiting factor in test time improvement of ICs is the number of chip terminals, such as pins or through silicon vias (TSVs) available for bulk vector transport in testing. In the conventional design, a chip terminal is only used to either send or receive data at any given time. In this article, a test accessibility architecture based on ternary encoded simultaneous bidirectional signaling (SBS), intended for use in parallel test access mechanism (TAM) in system on chip (SoC)-based designs, is proposed. This method enables the use of chip terminals to simultaneously send and receive test vectors, effectively doubling the per-pin efficiency during testing. Experiments show that this technique reduces the overall test time (OTT) by up to 53.6% as compared to conventional TAM design methods.

Integrated Vehicle Health Management (IVHM) aims to support Condition-Based Maintenance (CBM) by monitoring, diagnosing, and prognosing the health of the host system. One of the technologies required by IVHM to carry out its objectives is the means to emulate the functioning of the host system, and the concept of a Digital Twin (DT) was introduced in aerospace IVHM to represent the functioning of such a complex system. This paper aims to discuss the role played by DT in the field of IVHM. A DT is the virtual representation of any physical product, that is used to project the functioning of the product at a given instance. The DT is used across the lifecycle of any product, and its output can be customized depending upon the area of application. The DT is currently popular in industry because of the technologies like sensors, cloud computing, Internet of Things, machine learning, and advanced software, which enabled its development. This paper discusses what encompasses a DT, the technologies that support the DT, its applications across industries, and its development in academia. This paper also talks about how a DT can combine with IVHM technology to assess the health of complex systems like an aircraft. Lastly, this paper presents various challenges faced by industry during the implementation of a DT and some of the possible opportunities for future growth.