Leveraging Active Learning for Failure Mode Acquisition.

Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances in Natural Language Processing (NLP), efforts have been made to automate this process. However, it is not only time consuming, but extremely challenging to obtain maintenance records that list failure modes. Unsupervised learning methods such as topic modeling, clustering, and community detection are promising approaches for automatic processing of maintenance records to identify failure modes. However, the nascent state of NLP tools combined with incompleteness and inaccuracies of typical maintenance records pose significant technical challenges. As a step towards addressing these challenges, this paper proposes a framework in which online active learning is used to identify failure modes from maintenance records. Active learning provides a semi-supervised machine learning approach, allowing for a human in the training stage of the model. The hypothesis of this paper is that the use of a human to annotate part of the data and train a machine learning model to annotate the rest is more efficient than training unsupervised learning models. Results demonstrate that the model is trained with annotating less than ten percent of the total available data. The framework is able to achieve ninety percent (90%) accuracy in the identification of failure modes in test cases with an F-1 score of 0.89. This paper also demonstrates the effectiveness of the proposed framework with both qualitative and quantitative measures.

Sensor Selection Framework for Designing Fault Diagnostics System.

In a world of rapidly changing technologies, reliance on complex engineered systems has become substantial. Interactions associated with such systems as well as associated manufacturing processes also continue to evolve and grow in complexity. Consider how the complexity of manufacturing processes makes engineered systems vulnerable to cascading and escalating failures; truly a highly complex and evolving system of systems. Maintaining quality and reliability requires considerations during product development, manufacturing processes, and more. Monitoring the health of the complex system while in operation/use is imperative. These considerations have compelled designers to explore fault-mechanism models and to develop corresponding countermeasures. Increasingly, there has been a reliance on embedded sensors to aid in prognosticating failures, to reduce downtime, during manufacture and system operation. However, the accuracy of estimating the remaining useful life of the system is highly dependent on the quality of the data obtained. This can be enhanced by increasing the number of sensors used, according to information theory. However, adding sensors increases total costs with the cost of the sensors and the costs associated with information-gathering procedures. Determining the optimal number of sensors, associated operating and data acquisition costs, and sensor-configuration are nontrivial. It is also imperative to avoid redundant information due to the presence of additional sensors and the efficient display of information to the decision-maker. Therefore, it is necessary to select a subset of sensors that not only reduce the cost but are also informative. While progress has been made in the sensor selection process, it is limited to either the type of the sensor, number of sensors or both. Such approaches do not address specifications of the required sensors which are integral to the sensor selection process. This paper addresses these shortcomings through a new method, OFCCaTS, to avoid the increased cost associated with health monitoring and to improve its accuracy. The proposed method utilizes a scalable multi-objective framework for sensor selection to maximize fault detection rate while minimizing the total cost of sensors. A wind turbine gearbox is considered to demonstrate the efficacy of the proposed framework.

Haptic perception of users with low vision and their needs in haptic-incorporated user interfaces.

PURPOSE: This paper reports on research aimed at advancing understanding haptic capability and needs of users with low vision. The objective is to apply this understanding to the design of haptic-incorporated user interfaces. METHOD: Study 1 investigated the haptic perception between sighted participants and those with low vision through the magnitude estimation technique, and Study 2 explored the degree to which similar user interface needs were observed among the two vision groups. RESULTS: Overall, our findings indicate there was no significant difference between the two vision groups in terms of haptic perception and user interface needs. A few differences in user interface preference did exist, however, and designers should take these into account. CONCLUSIONS: Participants with low vision were a group who relied on their vision in everyday life instead of touch. Thus, their haptic capability was less likely to be enhanced via brain plasticity, which probably contributed to no significant difference in haptic-incorporated user interface needs. IMPLICATIONS FOR REHABILITATION: No significant different haptic capability and haptic user interface (UI) needs exists between cited participants and those with low vision. UI designers should take into consideration that a certain range of magnitude/type of haptic feedback is available to accommodate preferences of both vision groups, which would ultimately increase the likelihood of successfully developing universal designs.