The importance for Higher Education (HE) to embed the UN's (United Nations) SDG's (Sustainability Development Goals) into the IS (Information Systems) Curriculum

To succeed in today’s dynamic environment, Higher Education (HE) must continue to evolve and adapt at a rapid pace and create resilient graduates empowered with the knowledge, skills and abilities (KSA’s) to believe that they too “can” make a difference. It is timely for the HE sector to embrace and embed the United Nation's (UN’s) Sustainability Development Goals (SDG's) into the Information Systems (IS) curriculum and course offerings with a view to ensuring that graduates are cognisant of both the power of IS and IT and the importance of ensuring the equitable and ethical use of IS and IT. In particular, HE must ensure that we empower our IS Graduates to tackle and challenge complex problems in today's fast-paced digital world whilst at the same time, ensuring a fair and equitable outcome for all. Our ultimate aim as educators is to ensure that our graduates are both industry-ready and cognisant of their social and ethical responsibilities as outlined in the UN’s SDG’s

「デジタル・ナルシス、あるいは情報偏愛 : デジタル社会の人格・アイデンティティ」報告（主催：八重洲ブックセンター 協賛：聖学院大学出版会『デジタルの際 : 情報と物質が交わる現在地点』出版記念トークイベント）

Diagnosis and monitoring the condition of induction machines is critical for industries. Incipient fault detection has received a lot of attention in recent years. In this paper, a method based on complex wavelets is proposed for incipient fault detection and condition monitoring. A Complex wavelet-Support vector machine (SVM) classifier is developed which takes into account four conditions i.e.: healthy, turn fault (TF) under balanced supply conditions, voltage imbalance and interturn fault with voltage imbalance, both occurring at same time. The performance metrics show the ability of the technique to identify the fault at an early stage and it also provides additional information regarding which of the four conditions is prevailing at a given time. Voltage imbalance and turn fault are often confused. Both affect the performance of the machine and the unbalanced voltage condition considerably reduces the winding insulation life due to overheating. A comparison with standard Discrete Wavelet Transform (DWT) shows the effectiveness of the method in providing reliable information under variable supply-frequency conditions

Sensitive interturn fault diagnosis in induction machine using vibration analysis

Vibration analysis based interturn fault diagnosis and condition monitoring, using minimum computations is explored in this paper. The voltage imbalance is often confused with turn fault (TF), and has therefore been a challenge to diagnose the fault under varying supply-frequency and load conditions. The application of analytical wavelets in incipient fault diagnosis using vibration analysis, under such grid perturbations is highlighted in this paper. Once the feature extraction is performed using analytical wavelets, it is classified effortlessly using simple algorithm like KNN. The performance metrics brings out the benefit of using an efficient feature extraction for processing machine signatures. The turn fault can be identified at its very inception using this technique under varying conditions

Vibration analysis based interturn fault diagnosis in induction machines

A vibration analysis based interturn fault diagnosis of induction machines is proposed in this paper, using a neural-network-based scheme, constituting of two parts. The first part finds out the optimum network size of the probabilistic neural network (PNN) using the Orthogonal Least Squares Regression algorithm. This judges the size of the PNN, with an effort to reduce the computation. The feature extraction to model the PNN is made meaningful using dual tree complex wavelet transform (DTCWT), which is nearly shift invariant analytical wavelet transform, giving a true representation of the input space. In the second part, preprocessing using principal component analysis is suggested as an effective way to further reduce the dimension of the feature set and size of the PNN without compromising the performance. The sensitivity, specificity, and accuracy show that the vibration signatures capture the fault more effectively (especially by the axial and radial ones), under varying supply-frequency and load conditions. A comparison with traditional discrete wavelet transform proves the applicability of the proposed scheme. A comparative evaluation with feedforward neural network and naive Bayes scheme brings out the advantage of the proposed optimized DTCWT-PNN based technique over other machine learning approaches

Incipient turn fault detection and condition monitoring of induction machine using analytical wavelet transform

Diagnosis and monitoring the condition of induction machines and supply system is critical for industries. Incipient fault detection has received reasonable attention in recent years. In this paper, a method based on complex wavelets is proposed for incipient fault detection and condition monitoring. A complex wavelet-support vector machine (SVM) classifier-based method is developed which takes into account four conditions: healthy, turn fault (TF) under balanced supply conditions, voltage imbalance, and interturn fault with voltage imbalance, both occurring at the same time. The performance metrics show the ability of the technique to identify the fault at an early stage and it also provides additional information regarding which of the four conditions is prevailing at a given time. Voltage imbalance and turn fault are often confused. Both affect the performance of the machine and the unbalanced voltage condition considerably reduces the winding insulation life due to overheating. This necessitates the precise identification of the supply condition along with the fault diagnosis. A comparison of the proposed method with standard discrete wavelet transform (DWT) shows its effectiveness in providing reliable information under variable supply-frequency conditions. The proposed technique is also tested in presence of high resistance connections (HRCs), which shows its isolating capability

Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets

Embedded variable frequency induction motor drives are now an integral part of any industry due to their improved speed regulation and fast dynamic response. Hence, their diagnosis becomes vital to avoid downtimes and economic losses. In this paper, a technique based on a recent enhancement on wavelets, known as complex wavelets, is proposed for identifying multiple faults in vector controlled induction motor drives (VCIMDs). Radial, axial, and tangential vibrations are analyzed for diagnostic purpose. Initially, a relatively simple thresholding based method is investigated for feasibility of diagnosis under variable frequency and load conditions. In the second part, the feature extraction and classifier modeling are discussed, in which the nearly shift-invariant complex wavelet based model is compared with the discrete wavelet transform (DWT) for its applicability in detecting multiple faults. The fault conditions considered here are the most prominent ones such as interturn fault, interturn fault under progression, and bearing damage. Comparable performances of support vector machine (SVM) based models and simple technique based on k-nearest neighbor (k-NN) show the importance of efficient representation of input space by analytical wavelet based feature extraction. The performance indexes show the applicability of the scheme for industrial drives under variable frequencies and load conditions

Single-turn fault detection in induction machine using complex-wavelet-based method

Interturn short circuit is often confused with voltage imbalance in induction machines. Therefore, detection and classification of single-turn fault (TF) are becoming important in the presence of voltage imbalances, under various loading conditions. Substantial studies are conducted on the interturn fault detection, but a comprehensive method for classifying the faults at different operating points of the machine, under varying supply conditions, is still a challenge. This is a critical problem in industries since the induction motors form the major workhorses. The artificial-intelligence-based techniques are advanced methods in fault monitoring. This, when combined with optimization techniques, is expected to give improved and accurate results with minimum false alarms. In this paper, a technique is developed, based on recent developments in the wavelet-based analysis, particularly in the complex wavelet domain. The support vector machines are adopted for comparing the classification accuracy obtained using complex-wavelet- and standard discrete-wavelet-based methods. The receiver operating characteristic curves indicate that the fault detection, down to single turn, is feasible using a single current sensor

Broken rotor bar detection in variable frequency induction motor drives using wavelet energy based method

The fault detection of an induction motor operated by variable frequency drives is a present industrial need as most of the line fed machines are replaced by power electronic drives, due to their improved speed regulation and fast dynamic response. This paper presents a technique based on discrete wavelet transform (DWT) for diagnosis of the rotor fault. According to the application, the speed and the load can change and therefore the amplitudes of the fault signals are modulated. A support vector machine is used in the detection to learn the complex relationship between the various frequency bands of wavelet features to obtain a model which is impervious to such variations. The sensitivity, specificity, accuracy and the ROC graphs indicate the effectiveness of the proposed method for detecting the fault