Fault Detection Analysis in Ball Bearings using Machine Learning Techniques

The Bearing element is very essential component of any rotating equipment. Any defect in the bearings lead to instable performance of the machinery. To avoid such malfunction and breakdown of the machinery equipment due to misalignment is review critically in this research paper and various machine learning techniques to tackle the issue is highlighted. This review article finds the basis for developing an effective system in order to reduce the breakdown of machinery or equipment. Conventional Machine Learning methods, like Artificial neural network, Decision Tree, Random Forest, Support Vector Machines(SVM) have been applied to detecting categorizing fault, while the application of Deep Learning methods has ignited great interest in the industry

Fault Detection Analysis in Ball Bearings using Machine Learning Techniques

The Bearing element is very essential component of any rotating equipment. Any defect in the bearings lead to instable performance of the machinery. To avoid such malfunction and breakdown of the machinery equipment due to misalignment is review critically in this research paper and various machine learning techniques to tackle the issue is highlighted. This review article finds the basis for developing an effective system in order to reduce the breakdown of machinery or equipment. Conventional Machine Learning methods, like Artificial neural network, Decision Tree, Random Forest, Support Vector Machines(SVM) have been applied to detecting categorizing fault, while the application of Deep Learning methods has ignited great interest in the industry

A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings

The failure of rolling element bearings is one of the foremost causes of breakdown in rotary machinery. So far, a variety of vibration-based techniques have been developed to monitor the condition of bearings; however, the role of vibration behavior is rarely considered in the proposed techniques. This thesis presents an analytical study of a healthy rotor-bearing system to gain an understanding of the different categories of bearing vibration. In this study, a two degree-of-freedom model is employed, where the contacts between the rolling elements and races are considered to be nonlinear springs. The analytical investigations confirm that the nature of the inner ring oscillation depends on the internal clearance. A fault-free bearing with a small backlash exhibits periodic behavior; however, bearings categorized as having normal clearance oscillate chaotically. The results from the numerical simulations agree with those from the experiments confirming bearing’s chaotic response at various rotational speeds. Bearing faults generate periodic impacts which affect the chaotic behavior. This effect manifests itself in the phase plane, Poincare map, and chaotic quantifiers such as the Lyapunov exponent, correlation dimension, and information entropy. These quantifiers serve as useful indices for detecting bearing defects. To compare the sensitivity and robustness of chaotic indices with those of well-accepted fault detection techniques, a comprehensive investigation is conducted. The test results demonstrate that the Correlation Dimension (CD), Normalized Information Entropy (NIE), and a proposed time-frequency index, the Maximum Approximate Coefficient of Wavelet transform (MACW), are the most reliable fault indicators. A neuro-fuzzy diagnosis system is then developed, where the strength of the aforementioned indices are integrated to provide a more robust assessment of a bearing’s health condition. Moreover, a prognosis scheme, based on the Adaptive Neuro Fuzzy Inference System (ANFIS), in combination with a set of logical rules, is proposed for estimating the next state of a bearing’s condition. Experimental results confirm the viability of forecasting health condition under different speeds and loads

Energy Analytics for Infrastructure: An Application to Institutional Buildings

abstract: Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework. The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to 1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques. 2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms. 3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms. With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity

Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors

Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for rotating machinery (RM) have witnessed substantial advancements in recent decades, driven by the increasing demand for enhanced reliability, efficiency, and safety in industrial operations. CM of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, enhancing efficiency and reliability, and minimizing mechanical failures. While various FDD methods for RM have been developed, these predominantly focus on signal processing diagnostics techniques encompassing time, frequency, and time-frequency domains, intelligent diagnostics, image processing, data fusion, data mining, and expert systems. However, there is a noticeable knowledge gap regarding the specific review of image-based CM and FDD. The objective of this research is to address the aforementioned gap in the literature by conducting a comprehensive review of image-based intelligent techniques for CM and fault FDD specifically applied to induction motors (IMs). The focus of the study is to explore the utilization of image-based methods in the context of IMs, providing a thorough examination of the existing literature, methodologies, and applications. Furthermore, the integration of image-based techniques in CM and FDD holds promise for enhanced accuracy, as visual information can provide valuable insights into the physical condition and structural integrity of the IMs, thereby facilitating early FDD and proactive maintenance strategies. The review encompasses the three main faults associated with IMs, namely bearing faults, stator faults, and rotor faults. Furthermore, a thorough assessment is conducted to analyze the benefits and drawbacks associated with each approach, thereby enabling an evaluation of the efficacy of image-based intelligent techniques in the context of CM and FDD. Finally, the paper concludes by highlighting key issues and suggesting potential avenues for future research

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin