Sheng-Jen Hsieh

Manufacturing systems are becoming increasingly flexible, necessitating the adoption of new technologies that allow adaptations to a turbulent and complex modern market. Consequently, modern concepts of production systems require horizontal and vertical integration, extending across value networks and within a factory or production shop. The integration of these environments enables the acquisition of a substantial amount of data containing information pertaining to production, processes, and equipment located on the shop floor. When these data and information are processed and analyzed, they have the potential to reveal valuable insights and knowledge about the manufacturing systems, offering interpretive outcomes for strategic decision making. One of the opportunities presented in this context includes the implementation of predictive maintenance (PdM). However, industrial adoption of PdM is still relatively low. In this paper, the aim is to propose a methodology for selecting the main attributes (variables) to be considered in the instrumentation setup of rotating machines driven by electric motors to decrease the associated costs and the time spent defining them. For this, the most well-known data science and machine learning algorithms are investigated to choose the one most adequate for this task. For the experiments, different testing scenarios were proposed to detect the different possible types of anomalies, such as uncoupled, overloaded, unbalanced, misaligned, and normal. The results obtained show how these algorithms can be effective in classifying the different types of anomalies and that the two models that presented the best accuracy values were k-nearest neighbor and multi-layer perceptron.

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.