Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instru-ment to efficiently synthesize faulty conditions and mass-produce data to develop fault detection and diagnosis algorithms. The first contribution is the formalization of a new approach called Robotic Condition Synthesis, which shifts the traditionally labor-intensive task of condition synthesis to a robot-based force control task. The second contribution is developing a new robotic manipulator, which is more effective than current lab-grade robots for the tasks involved in the Robotic Condition Synthesis. The third contribution is empirical evidence of the superiority of this new robot in performing the Robotic Condition Synthesis tasks. This study also explores the potential of the new robot by conducting a three-dimensional system identification of a rotordynamic plant, which lays the foundation for more advanced Robotic Condition Synthesis policies in the future.

The time-series datasets commonly applied for anomaly detection research showcase specific suboptimal properties. This work novelly conceptualizes condition state synthesis to improve the data-synthetic pipeline of an anomalous-event dataset. We demonstrate two technical contributions in this study. First, we propose a methodology to formulate, accelerate and enrich the condition state synthetic process. The proposed method includes three critical phases: analysis of a rotodynamic plant, systematic design of its condition state space, and development of a Markovian model for controlled state transitions. Second, a Rotodynamic System with Synthetic Anomaly dataset is constructed. It is a large-scale time-series dataset featuring controlled, abundant and diverse anomalous condition states, and per-time-step condition state labels. A comprehensive learning-based case study is conducted to illustrate that these unique features tangibly benefit anomaly detection research. Potential usages of the proposed dataset as an anomaly detection study benchmark are discussed.

Data synthesizing is crucial for data-driven anomaly prognostics on physical machines. We propose the first general-purpose anomalous scenario synthesizer, GPASS, for rotary equipment. More specifically, we present a design of implementing modular rotational damping, large lateral force, with high-frequency range capability as fundamental modes of physical inputs. The GPASS is a general-purpose platform that can impose inputs independently or jointly, and generate an extensive range of anomalous scenarios on the same subject. Finally, it has the capability of capturing multi-variate sensor readings on the same anomalous event. Experimental results demonstrate that the synthesizer can dynamically and accurately introduce lateral force at specified magnitudes and frequencies, proving the effectiveness of the proposed device.

Virtually every mechanized form of transportation, power generation system, industrial equipment, and robotic system has rotating shafts. As the shaft is often the main means of mechanical power transmission, measuring the torque, speed, vibration, and bending of the shaft can be used in many cases to access device performance and health and to implement controls. This paper proposes a shaft sensor that measures all of these phenomena with reasonable accuracy while having a low cost and simple installation process. This sensor transfers strain from the shaft and amplifies it to increase sensitivity. Furthermore, this sensor requires no components to be in the stationary reference frame, allowing the entire device to rotate with the shaft. A prototype is presented. Experimental results illustrate the effectiveness of the proposed system.