Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms

Early fault detection is a challenge in gear fault diagnosis. In particular, efficient feature extraction and feature selection is a key issue to automatic condition monitoring and fault diagnosis processes. In order to focus on those issues, this paper presents a study that uses ensemble empirical mode decomposition (EEMD) to extract features and hybrid binary bat algorithm (HBBA) hybridized with machine learning algorithm to reduce the dimensionality as well to select the predominant features which contains the necessary discriminative information. Efficiency of the approaches are evaluated using standard classification metrics such as Nearest neighbours, C4.5, DTNB, K star and JRip. The gear fault experiments were conducted, acquired the vibration signals for different gear states such as normal, frosting, pitting and crack, under constant motor speed and constant load. The proposed method is applied to identify the different gear faults at early stage and the results demonstrate its effectiveness

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance

Induction motors fault diagnosis using machine learning and advanced signal processing techniques

In this thesis, induction motors fault diagnosis are investigated using machine learning and advanced signal processing techniques considering two scenarios: 1) induction motors are directly connected online; and 2) induction motors are fed by variable frequency drives (VFDs). The research is based on experimental data obtained in the lab. Various single- and multi- electrical and/or mechanical faults were applied to two identical induction motors in experiments. Stator currents and vibration signals of the two motors were measured simultaneously during experiments and were used in developing the fault diagnosis method. Signal processing techniques such as Matching Pursuit (MP) and Discrete Wavelet Transform (DWT) are chosen for feature extraction. Classification algorithms, including decision trees, support vector machine (SVM), K-nearest neighbors (KNN), and Ensemble algorithms are used in the study to evaluate the performance and suitability of different classifiers for induction motor fault diagnosis. Novel curve or surface fitting techniques are implemented to obtain features for conditions that have not been tested in experiments. The proposed fault diagnosis method can accurately detect single- or multi- electrical and mechanical faults in induction motors either directly online or fed by VFDs. In addition to the machine learning method, a threshold method using the stator current signal processed by DWT is also proposed in the thesis

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

LHD vibrations analysis and numerical modeling during operations

Load-haul-dump vehicles (LHDs) are extensively used as primary loaders in mining operations. LHDs have proven to be vigorous, extremely productive and reliable in mining applications. They have a wide range of tramming capacities that have enabled them to become an essential component in the hard rock mining industry. Increased mining economic challenges and global competition means the mining industry has to maximize productivity by cutting down operating and capital costs. Also, improvements in safety standards have led to the demand for safer and efficient machines. LHD operators are at a high risk of whole-body vibrations (WBVs) exposure leading to musculoskeletal disorders (MSDs) over long exposure periods, and elevated lower back and neck injuries. Thus, there is a health and safety concern among LHD operators. Despite manufacturer’s emphasis on ergonomics, there is lack of adequate fundamental vibration models of large mining equipment accessible to the public. This research focused on developing valid analytical and numerical models for determining the vibration propagation in LHDs. Also, this research pioneered the development and analysis of comprehensive dynamic virtual models of LHDs with detailed vibration analysis of the operator-seat interface. The introduced LHD virtual prototype has a total of 24-DOF and captures the complex vibration mechanics of the LHD, with emphasis on vibrations reaching the operator seat-interface in the three dimensions (3D), x, y and z-directions. The RMS accelerations recorded at the operator-seat interface are 0.62 m/s² in the x-direction, 0.51 m/s² in the y-direction, and 1.01 m/s² in the z-direction which exceed the ISO-2631 comfort level --Abstract, page iii

The Use of Advanced Soft Computing for Machinery Condition Monitoring

The demand for cost effective, reliable and safe machinery operation requires accurate fault detection and classification. These issues are of paramount importance as potential failures of rotating and reciprocating machinery can be managed properly and avoided in some cases. Various methods have been applied to tackle these issues, but the accuracy of those methods is variable and leaves scope for improvement. This research proposes appropriate methods for fault detection and diagnosis. The main consideration of this study is use Artificial Intelligence (AI) and related mathematics approaches to build a condition monitoring (CM) system that has incremental learning capabilities to select effective diagnostic features for the fault diagnosis of a reciprocating compressor (RC). The investigation involved a series of experiments conducted on a two-stage RC at baseline condition and then with faults introduced into the intercooler, drive belt and 2nd stage discharge and suction valve respectively. In addition to this, three combined faults: discharge valve leakage combined with intercooler leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with suction valve leakage were created and simulated to test the model. The vibration data was collected from the experimental RC and processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. A large number of potential features are calculated from the time domain, the frequency domain and the envelope spectrum. Applying Neural Networks (NNs), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs) which integrate with Genetic Algorithms (GAs), and principle components analysis (PCA) which cooperates with principle components optimisation, to these features, has found that the features from envelope analysis have the most potential for differentiating various common faults in RCs. The practical results for fault detection, diagnosis and classification show that the proposed methods perform very well and accurately and can be used as effective tools for diagnosing reciprocating machinery failure