A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node

Acoustic Condition Monitoring & Fault Diagnostics for Industrial Systems

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focusing on noise rejection and fault classification. Usually, techniques are fine-tuned to maximise diagnostic performance for the given problem. The majority investigate mechanical fault modes, particularly Roller Element Bearings (REBs), owing to the mechanical impacts producing detectable acoustic waves. The first contribution of this project is a suitability study into the use of low-cost consumer-grade acoustic sensors for fault diagnostics of six different REB health conditions, comparing against vibration measurements. Experimental results demonstrate superior acoustic performance throughout but particularly at lower rotational speed and axial load. Additionally, inaccuracies caused by dynamic operational parameters (speed in this case), are minimised by novel multi-Support Vector Machine training. The project then expands on existing work to encompass diagnostics for a previously unreported electrical fault mode present on a Brush-Less Direct Current motor drive system. Commonly studied electrical faults, such as a broken rotor bar or squirrel cage, result from mechanical component damage artificially seeded and not spontaneous. Here, electrical fault modes are differentiated as faults caused by issues with the power supply, control system or software (not requiring mechanical damage or triggering intervention). An example studied here is a transient current instability, generated by non-linear interaction of the motor electrical parameters, parasitic components and digital controller realisation. Experimental trials successfully demonstrate real-time feature extraction and further validate consumer-grade sensors for industrial system diagnostics. Moreover, this marks the first known diagnosis of an electrically-seeded fault mode as defined in this work. Finally, approaching an industry-ready diagnostic system, the newly released PYNQ-Z2 Field Programmable Gate Array is used to implement the first known instance of multiple feature extraction algorithms that operate concurrently in continuous real-time. A proposed deep-learning algorithm can analyse the features to determine the optimum feature extraction combination for ongoing continuous monitoring. The proposed black-box, all-in-one solution, is capable of accurate unsupervised diagnostics on almost any application, maintaining excellent diagnostic performance. This marks a major leap forward from fine-tuned feature extraction performed offline for artificially seeded mechanical defects to multiple real-time feature extraction demonstrated on a spontaneous electrical fault mode with a versatile and adaptable system that is low-cost, readily available, with simple setup and operation. The presented concept represents an industry-ready all-in-one acoustic diagnostic solution, that is hoped to increase adoption of acoustic methods, greatly improving diagnostics and minimising catastrophic failures

Controller Platform Design and Demonstration for an Electric Aircraft Propulsion Driv

With the growth in the aerospace industry there has been a trend to optimize the performance of an aircraft by reducing fuel consumption and operational cost. Recent advancements in the field of power electronics have pushed towards the concepts of hybrid electric aircraft also known as more electrical aircrafts. In this work, a custom controller board for an electric aircraft propulsion drive was designed to drive a permanent magnet synchronous motor. Design of the controller board required knowledge of the topology selection and power module selections. Simulations of the system were performed using MATLAB/Simulink to analyze the overall performance of the selected topology. Implementation of the control algorithm was tested on the hardware prototype of a three-phase, two-level voltage source inverter. Complete testing of the system at high power was accomplished; thus, demonstrating the inverter’s ability to operate at the desired power level

Detection of Lubrication Starvation in Ball Bearings by Means of Lateral and Torsional Vibrations

Considerable research has been conducted in fault detection and diagnosis for ball bearings but it has been focused on detecting the early stages of imminent faults due to fatigue. In reality, most bearings never reach the expected life or fatigue cycles due to problems related to maintenance or installation. This work studies lubrication starvation, which is one of the main causes of premature bearing failure. This work focuses on explaining the origin of the frequency indicators based on proposed bearing models. The validation of one of the models is achieved through a series of experiments measuring lateral vibrations and proving that the characteristic signal originates from the gap created by the absence of lubricant. Additionally, several lateral vibration indicators are compared for fault detection concluding that Fast-Kurtogram is the best technique for detecting lubrication starvation. A further diagnosis using envelope analysis verifies another model that proposes the ball pass frequency of the outer race (BPFO) as the main indicator of lubrication starvation in the frequency domain. An alternative method based on torsional vibrations at the shaft is additionally evaluated. A sensor based on the Time Interval Measurement System (TIMS) is developed using a field programmable gate array (FPGA) and a quadrature encoder to measure torsional vibrations. From the simulation of torque friction, the torque is found to not be significant compared to the driving torque of industrial motors. Hence the use of torsional vibrations as a mean to detect lubrication starvation is limited to applications in which bearing friction could impact the performance of the rotor drastically. The results are verified by the experiments in which the torsional vibrations are not able to detect changes in lubrication conditions. Finally, an energy analysis is presented to study the impact of lubrication starvation in the motor efficiency, which could be an economical motivator to encourage the research of condition monitoring of lubrication starvation. Lubrication starvation decreases the efficiency by dissipating power as heat and is dependent on the speed and static load rather than the load factor of the motor