Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise the winding temperature. With the development of new technologies and researches on non-destructive techniques (NDT) for fault diagnosis in IMs, it shows relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this work is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrogram. The piezoelectric sensor makes NDT feasible as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analysis of acoustic emission signals. For this NDT, two sensors were coupled on both sides of the induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different UV levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain the desired identification, which confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

Advances in permanent magnet technology has seen many examples in the literature of sensor applications of low field magnetic resonance and these are usually based on the measurement of relation times. Whilst most reports are either in the 10-20MHz range or in the earths field, measurements at below 1MHz are beginning to become more widespread. This range of frequencies is below the need for careful radio frequency electronics design but above the audio domain where extensive commercial hardware is readily available and represents an interesting cross over. Most wideband commercial NMR spectrometers do not include the pulse power amplifier, duplexer and preamplifier as these depend on the frequency range over which they are to be used. In this work we demonstrate that the humble operational amplifier in the most simple form of an inverting design, using only two resistors and decoupling, can effectively provide this ‘front end’ electronics. A Linear Technology LT1363 amplifier provides the pulse power amplifier and a Linear Technology LT1222 the preamplifier. Example data using a CPMG sequence and an olive oil sample is presented from which T2(effective) can be determined and the signal intensity as a function of repetition time to give an estimate for the spin lattice relaxation time T1. The low powers used mean crossed Ge diodes provide an excellent duplexer and it has been found to be ideally suited to battery powered applications.

The leaks in roofs and cracks in walls of buildings are common and need immediate attention. The roof leaks or cracks lead to water seepage resulting in structural damage to the ceiling wall. In this work, the roof leaks or cracks are identified using the proposed thermal sensor-based decision support system. Further, the thermal camera is interfaced with a handy single on-board computer. The supervised machine learning algorithm is coded inside the single on-board computer and the thermal images captured using the thermal camera is utilized for the fault identification. Further, the trained network is tested using a new set of thermal images for identification of faults. Results demonstrate that the proposed system is efficient in locating and identification of faults. Since the single on-board has an inbuilt Wi-Fi, the decision support can be stored in the cloud server with a specific unique Uniform Resource Locator (URL) address. Also, by accessing the appropriate URL, the decision support system can be accessed from remote locations.

Over the decades, visual inspection has been adopted as a means to monitor infrastructure health. While visual inspection provides insights on bridge condition, it has been generally agreed that it is insufficient and inefficient. This has called for creating autonomous, robust, continuous and quantitative Structural Health Monitoring systems to detect damage early using machine learning algorithms and monitor future condition. Various methods have been explored that associate changes in condition with changes in the structure vibration characteristics. These methods have been mostly tested on laboratory specimens experiencing simulated damage. There is need for more validation of these SHM methods on in-situ structures experiencing real damage under operational and environmental conditions. This paper summarizes a full-scale experiment exploring bridge damage detection effectiveness under variable traffic loads. Three different types of damage were introduced into a full-scale, bridge deck mock-up. These included crash-induced bridge barrier damage, controlled barrier damage, and damage to the deck slab. At the end of each introduced damage case, bridge response to the multiple passages using specific vehicles specifications was recorded. Data was extracted and analyzed to identify damage using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as damage sensitive features. The extracted damage features were thereafter used as input for unsupervised learning (novelty detection). One interesting observation was how PCA revealed possibly significant damage after crash, which under visual inspection appeared to be minor cracking. Novelty detection using PCA as its damage feature was shown to provide robust damage detection irrespective of load, speed variation and signal noise levels.

Transmission and distribution play a vital role in delivering electricity. Presence of any fault in these systems may stop the delivery of electricity, which may create a huge problem in today’s world. Hence, fault detection has become essential for delivering uninterrupted power supply. In this work, a portable and intelligent system is designed, and the fault detection on underground transmission lines is done using developed hardware system. Also, the proposed system has a thermal camera which is an 8x8 array of infrared thermal sensors interfaced with a system-on-chip device, which collects the real-time thermal images when connected to the device. Further, the thermal camera returns an array of 64 individual infrared temperature readings, of the transmission line and locates the point of damage which might occur due to the aging of conductor insulation, physical force, etc. Also, 200 images with thermal information from the different instances and directions are utilized to train the adapted machine learning algorithm. The python software is utilized to code the machine learning algorithm inside the system-on-chip device. The convolutional neural network-based machine learning algorithm is adopted and it is validated using various performance metrics such as accuracy, sensitivity, specificity, precision, negative predicted value, and F1_score. Results demonstrate that the proposed hardware is highly capable of locating faults in underground transmission lines.