Torque measurements can significantly enhance control and monitoring loops for many mechatronic and aerospace applications. A typical challenge is to justify incorporating a torque sensor in terms of cost, system complexity, and reliability. Recently, sensorless torque estimation methods have been developed for robotic joints that include harmonic drive transmissions (HDTs). The principle is based on their relatively low torsional stiffness, which allows for estimating the transmitted torque by measuring the torsional angles (via existing joint encoders) and a compliance model. However, these methods are based on nonlinear models that are difficult to identify and tune. In this study, a simplified torque estimation method is introduced based on the structural damping friction of the HDTs. The structural damping can be correlated to the HDT torque using a simplified linear dynamic model and torsional rate measurements. Experimental results have validated the proposed method, using a robotic joint setup with an external torque sensor that has been previously utilized for testing several torque estimation methods.

Future aircraft architectures will incorporate more energy-efficient electromechanical actuators (EMA) for flight controls actuation. Development of reliable health monitoring techniques for EMAs promises to maintain or even increase the overall availability and safety of these new aircraft designs. When it comes to EMAs and similar mechanisms, certain fault types clearly manifest themselves through loss of functionality. Other faults, referred to as latent, do not immediately result in a significantly compromised actuator performance, thus making them challenging to detect. This paper presents a new vibration-based hybrid technique for detecting latent EMA faults without needing an initial stage of fault feature learning. The two faults considered in the study are a high-criticality jam and a low-criticality spall (metal flaking) in the actuator ballscrew mechanism. The actuator position is used to resample variable-speed vibration measurements of a single accelerometer into constant-rate measurements. A set of health characterization signatures is derived theoretically based on the EMA ballscrew kinematics. These theoretical signatures are compared with the signatures extracted from vibration signals measured experimentally on the EMA test articles. The vibration signatures approach is also compared to the diagnostic approach based on EMA motor current measurements. The ability to detect and classify latent faults early as high-or low-critical can improve maintenance planning and increase aircraft dispatch reliability. The technique has been validated on fault-injected data sets collected on the NASA Ames Research Center Flyable Electro-Mechanical Actuator (FLEA) test stand.

Vibration-based fault diagnosis has been utilized as a reliable method for identifying ball bearings health since the 1970s. Recently, there has been an increased research effort to develop methods for fault quantification with the aim of estimating the fault size to allow the service life of a ball bearing to be extended beyond the detection stage. These studies have shown that the vibration signal from a localized spall (e.g. fatigue defect) in a ball bearing exhibits features corresponding to two main events, namely, the entry into and the exit from the spall. The time span between these two events is correlated with the spall size. Studies have shown that the entry into the spall is the more challenging event to identify, which often requires extensive signal processing techniques. This paper introduces an automated vibration-based technique for estimating the size of a spall in a ball bearing under axial loading conditions similar to those of linear electro-mechanical actuators. This technique is based on the extraction of the entry/exit events from the vibrational jerk, which are numerically determined from accelerometer data. The differentiation of the acceleration data to estimate jerk signal is performed using a variant of Savitzky–Golay (SG) differentiators, which provide enhancement for the detection of the entry and exit points. Sensible spall size estimations have been achieved for 24 different scenarios of fault sizes, rotor speeds and loads measured on a test rig provided by DLR (German Aerospace Center).

Electro-mechanical actuators (EMAs) are a primary actuation technology for unmanned aerial vehicles (UAVs). Intensive research has been conducted for designing and evaluating fault-tolerant EMAs for flight controls of UAVs to ensure their compliance with new airworthiness requirements for safe operation over civilian zones. The state-of-the-art research involves several fault-tolerant architectures for EMAs based on parallel electric motors or a single motor with internal fault-tolerant features. In this study, a fault-tolerant architecture is introduced, comprised of two serial electric motors driven by two isolated controllers and a health monitoring system. The procedures of developing various fault-tolerant features are discussed with a deep focus on designing health monitoring functions and evaluating their influence on the overall actuator stability and availability. This work has been conducted and evaluated based on operational data for ALAADy: a heavy gyrocopter-type UAV at DLR (German Aerospace Center).

Flight control electro-mechanical actuators (EMAs) are among the primary onboard systems that significantly influence the reliability and safety of unmanned aerial vehicles. Recent reliability studies have shown that the ball-screw element of a flight control EMA is subject to oscillating operating conditions that may initiate rapid degradation, such as fatigue spall defects. Accordingly, detecting and quantifying such faults are crucial for developing efficient fault prognostic and remaining useful life estimation capabilities. In this study, a vibration-based fault quantification method is developed to quantify the fatigue faults of a ball-screw mechanism of an EMA. The method is based on identifying the ball passing instants through a localized surface defect on the vibrational jerk rather than the vibrational acceleration measurement. The jerk is numerically determined from conventional accelerometers using a Savitzky–Golay differentiator. This method was successfully tested for ball bearings and it is adjusted in this paper for ball-screw faults. The experimental validation is investigated on a set of fault-seeded samples on NASA’s Ames Research Center Flyable Electro-Mechanical Actuator test stand.

Ball bearings are essential components of electromechanical systems, and their failures significantly affect the service lifetime of these systems. For highly reliable and safety-critical electromechanical systems in energy and aerospace sectors, early bearing fault detection and quantification are crucial. The vibration measurements of bearing fatigue faults, i.e., spalls, are typically induced by multiple excitation mechanisms depending on the fault size and the operating conditions. This data article contains vibration datasets for faulty ball bearings, including the common vibration excitation mechanisms for various fault sizes and operating conditions. These faults are artificially seeded on bearing races by a precise machining process to emulate realistic fatigue faults. This data article is beneficial for better understanding the vibration signal characteristics under different fault sizes and for validating condition monitoring methods for various industrial and aerospace applications.