Showing papers on "Condition monitoring published in 2011"

A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

Abstract: Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.

Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)

Abstract: Condition monitoring of induction motors is a fast emerging technology in the field of electrical equipment maintenance and has attracted more and more attention worldwide as the number of unexpected failure of a critical system can be avoided. Keeping this in mind a bearing fault detection scheme of three-phase induction motor has been attempted. In the present study, Support Vector Machine (SVM) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the frame vibrations during start-up. CWT has not been widely applied in the field of condition monitoring although much better results can been obtained compared to the widely used DWT based techniques. The encouraging results obtained from the present analysis is hoped to set up a base for condition monitoring technique of induction motor which will be simple, fast and overcome the limitations of traditional data-based models/techniques.

Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems

Abstract: Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity.This book is a sequel of the book Fault-Diagnosis Systems published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as:Electrical drives (DC, AC)Electrical actuatorsFluidic actuators (hydraulic, pneumatic)Centrifugal and reciprocating pumpsPipelines (leak detection)Industrial robotsMachine tools (main and feed drive, drilling, milling, grinding)Heat exchangersAlso realized fault-tolerant systems for electrical drives, actuators and sensors are presented.The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful.The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.

State-of-the-Art Predictive Maintenance Techniques

Abstract: Condition-based maintenance techniques for industrial equipment and processes are described in this paper together with examples of their use and discussion of their benefits. These techniques are divided here into three categories. The first category uses signals from existing process sensors, such as resistance temperature detectors (RTDs), thermocouples, or pressure transmitters, to help verify the performance of the sensors and process-to-sensor interfaces and also to identify problems in the process. The second category depends on signals from test sensors (e.g., accelerometers) that are installed on plant equipment (e.g., rotating machinery) in order to measure such parameters as vibration amplitude. The vibration amplitude is then trended to identify the onset of degradation or failure. This second category also includes the use of wireless sensors to provide additional points for collection of data or allow plants to measure multiple parameters to cover not only vibration amplitude but also ambient temperature, pressure, humidity, etc. With each additional parameter that can be measured and correlated with equipment condition, the diagnostic capabilities of the category can increase exponentially. The first and second categories just mentioned are passive, which means that they do not involve any perturbation of the equipment or the process being monitored. In contrast, the third category is active. That is, the third category involves injecting a test signal into the equipment (sensors, cables, etc.) to measure its response and thereby diagnose its performance. For example, the response time of temperature sensors (RTDs and thermocouples) can be measured by the application of the step current signal to the sensor and analysis of the sensor response to the application of the step current. Cable anomalies can be located by a similar procedure referred to as the time domain reflectometry (TDR). This test involves a signal that is sent through the cable to the end device. Its reflection is then recorded and compared to a baseline to identify impedance changes along the cable and thereby identify and locate anomalies. Combined with measurement of cable inductance (L), capacitance (C), and loop resistance (R), or LCR testing, the TDR method can identify and locate anomalies along a cable, identify moisture in a cable or end device, and even reveal gross problems in the cable insulation material. There are also frequency domain reflectometry (FDR) methods, reverse TDR, trending of insulation resistance (IR) measurement, and other techniques which can be used in addition to or instead of TDR and LCR to provide a wide spectrum of tools for cable condition monitoring. The three categories of techniques described in this paper are the subject of current research and development projects conducted by the author and his colleagues at the AMS Corporation with funding from the U.S. Department of Energy (DOE) under the Small Business Innovation Research (SBIR) program.

A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

Abstract: This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering

Abstract: Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors.

The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

Abstract: Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified; however, they generally deal with a single fault only. Instead, in real induction machines, the case of multiple faults is common. When multiple faults exist, vibration and current are excited by several fault-related frequencies combined with each other, linearly or nonlinearly. Different techniques have been proposed for the diagnosis of rotating machinery in literature, where most of them are focused on detecting single faults and few works deal with the diagnosis and identification of multiple combined faults. The contribution of this paper is the development of a condition-monitoring strategy that can make accurate and reliable assessments of the presence of specific fault conditions in induction motors with single or multiple combined faults present. The proposed method combines a finite impulse response filter bank with high-resolution spectral analysis based on multiple signal classification for an accurate identification of the frequency-related fault. Results show the methodology potentiality as a deterministic detection technique that is suited for detecting multiple features where the fault-related frequencies are very close to those analytically reported in literature.

Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

Abstract: A K-means clustering approach is proposed for the automated diagnosis of defective rolling element bearings. Since K-means clustering is an unsupervised learning procedure, the method can be directly implemented to measured vibration data. Thus, the need for training the method with data measured on the specific machine under defective bearing conditions is eliminated. This fact consists the major advantage of the method, especially in industrial environments. Critical to the success of the method is the feature set used, which consists of a set of appropriately selected frequency-domain parameters, extracted both from the raw signal, as well as from the signal envelope, as a result of the engineering expertise, gained from the understanding of the physical behavior of defective rolling element bearings. Other advantages of the method are its ease of programming, simplicity and robustness. In order to overcome the sensitivity of the method to the choice of the initial cluster centers, the initial centers are selected using features extracted from simulated signals, resulting from a well established model for the dynamic behavior of defective rolling element bearings. Then, the method is implemented as a two-stage procedure. At the first step, the method decides whether a bearing fault exists or not. At the second step, the type of the defect (e.g. inner or outer race) is identified. The effectiveness of the method is tested in one literature established laboratory test case and in three different industrial test cases. Each test case includes successive measurements from bearings under different types of defects. In all cases, the method presents a 100% classification success. Contrarily, a K-means clustering approach, which is based on typical statistical time domain based features, presents an unstable classification behavior.

A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events

Abstract: This paper deals with the condition-based maintenance of single-unit systems which are subject to the competing and dependent failures due deterioration and traumatic shock events. The main aim is to provide a model to assess the value of condition monitoring information for the maintenance decision-making. A condition-based periodic inspection/replacement policy is developed and compared with a benchmark time-based block replacement policy. Numerical results show that it is indeed useful to follow closely the actual evolution of the system to adapt the maintenance decisions to the true system state to improve the performance of maintenance policies. The analysis of the maintenance costs savings can be used to justify or not the choice to implement a policy based on condition monitoring information and to invest in condition monitoring devices.

Distributed road surface condition monitoring using mobile phones

Abstract: The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

Integrated Direct/Indirect Adaptive Robust Control of Hydraulic Manipulators With Valve Deadband

Abstract: In this paper, an integrated direct indirect adaptive robust control (DIARC) algorithm is proposed for an electro-hydraulic manipulator with unknown valve deadband to improve the achievable output-tracking performance. The controller design for such a system is nontrivial due to various factors, such as nonsmooth static and Coulomb friction model uncertainties arising from the use of a simplified proportional-flow-valve model, and other unknown disturbances present in the system. Furthermore, when an unknown input-valve deadband is present, the controller performance can deteriorate if it is not taken care of explicitly. This paper recognizes the fact that though unknown valve deadband nonlinearity is not globally linearly parameterizable, it can still be linearly parameterized during most of the working ranges. Hence, by using an indirect estimation algorithm with online condition monitoring, accurate estimates of the unknown deadband parameters are obtained for an improved control performance. Comparative experimental results for motion control of an electro-hydraulic manipulator with two different valves having deadband illustrate the effectiveness of the proposed algorithm.

Effect of number of features on classification of roller bearing faults using SVM and PSVM

Abstract: Bearings in the machines are the major components of interest for condition monitoring. Their failure causes increase in down time and maintenance cost. A possible solution to the problem is developing an on-line condition monitoring system. The vibration characteristics can be a determining factor that will reveal the condition of the bearing parts. Visual inspection of frequency-domain features of the vibration signals may be sufficient to identify the faults, but it requires large domain knowledge and it is a function of speed. Automatic diagnostic techniques allow relatively unskilled operators to make important decisions. In this context, machine learning algorithms have been successfully used to solve the problem with the help of vibration signals. The machine learning procedure has three important phases: feature extraction, feature selection and feature classification. Feature selection involves identifying the good features that contributes greatly for classification and determining the number of such features. Often researchers overlook the later issue and arbitrarily choose the number of features. As there is no science that will tell the right number of features, for a given problem, an extensive study is needed to find the optimum number of features and this paper presents the results of such a study using SVM and PSVM classifiers for statistical and histogram features of time domain signal. The findings are very interesting and challenging; some useful conclusions were drawn and presented.

An Impedance Identification Approach to Sensitive Detection and Location of Stator Turn-to-Turn Faults in a Closed-Loop Multiple-Motor Drive

Abstract: A single closed-loop inverter drive with multiple motors connected to it is a type of drive topology commonly used in steel processing industry, electric railway systems, and electric vehicles. However, condition monitoring for this type of drive configuration remains largely unexplored. This paper proposes an impedance identification approach to detect and locate the stator turn-to-turn fault in a multiple-motor drive system. Sensitive and fast fault detection is achieved by utilizing the characteristics of current regulators in the motor controller. Experimental results show that the proposed method can reliably detect and locate the stator turn fault on two shaft-coupled 5-hp induction machines under different operating conditions and fault levels with no need of any machine parameters. Although originally developed for multiple-motor drives, the detection scheme can also be directly applied to most of the conventional closed-loop induction motor drives.

Machine health prognostics using survival probability and support vector machine

Abstract: Prognostic of machine health estimates the remaining useful life of machine components. It deals with prediction of machine health condition based on past measured data from condition monitoring (CM). It has benefits to reduce the production downtime, spare-parts inventory, maintenance cost, and safety hazards. Many papers have reported the valuable models and methods of prognostics systems. However, it was rarely found the papers deal with censored data, which is common in machine condition monitoring practice. This work concerns with developing intelligent machine prognostics system using survival analysis and support vector machine (SVM). SA utilizes censored and uncensored data collected from CM routine and then estimates the survival probability of failure time of machine components. SVM is trained by data input from CM histories data that corresponds to target vectors of estimated survival probability. After validation process, SVM is employed to predict failure time of individual unit of machine component. Simulation and experimental bearing degradation data are employed to validate the proposed method. The result shows that the proposed method is promising to be a probability-based machine prognostics system.

FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

Abstract: The development of monitoring systems for rotating machines is the ability to accurately detect different faults in an incipient state. The most popular rotating machine in industry is the squirrel-cage induction motor, and the failure on such motors may have severe consequences in costs, product quality, and safety. Most of the condition-monitoring techniques for induction motors focus on a single specific fault. The identification of two or more combined faults has been rarely considered, in spite of being a very usual situation in real rotary machines. On the other hand, information entropy is a signal processing technique that has recently proved its suitability for fault detection on induction motors, and fuzzy logic analysis has extensively been used in combination with several processing techniques in improving the diagnosis of a single isolated fault. The contribution of this paper is a novel methodology that is suitable for hardware implementation, which merges information entropy analysis with fuzzy logic inference to identify faults like bearing defects, unbalance, broken rotor bars, and combinations of faults by analyzing one phase of the induction motor steady-state current signal. The proposed methodology shows satisfactory results that prove its suitability for online detection of single and multiple combined faults in an automatic way through its hardware implementation in a field programmable gate array device.

Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis

Abstract: The objective of this work is to develop techniques to automate the condition-based maintenance procedure. It is observed that vibration signals are capable of alarming the malfunctions in machineries. In order to overcome the shortcomings in the traditional vibration analysis using time-domain and frequency-domain features, two new approaches based on wavelet transform, artificial neural network and fuzzy rules are proposed for detecting and localizing defects in rolling element bearings. The two expert systems are developed and tested with the use of vibration signals collected from the bearing housing of an experimental setup. Experiment results show that the proposed approaches are sensitive and reliable in detecting defects on the outer race, inner race and rolling elements of bearings. The proposed approaches may be used for other fault diagnoses such as gear faults, coupling faults, belts in industries. It is also expected from the obtained results that the generalized defect detection will be easi...

A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems

Abstract: The reference frame theory constitutes an essential aspect of electric machine analysis and control. In this study, apart from the conventional applications, it is reported that the reference frame theory approach can successfully be applied to real-time fault diagnosis of electric machinery systems as a powerful toolbox to find the magnitude and phase quantities of fault signatures with good precision as well. The basic idea is to convert the associated fault signature to a dc quantity, followed by the computation of the signal's average in the fault reference frame to filter out the rest of the signal harmonics, i.e., its ac components. As a natural consequence of this, neither a notch filter nor a low-pass filter is required to eliminate fundamental component or noise content. Since the incipient fault mechanisms have been studied for a long time, the motor fault signature frequencies and fault models are very well-known. Therefore, ignoring all other components, the proposed method focuses only on certain fault signatures in the current spectrum depending on the examined motor fault. Broken rotor bar and eccentricity faults are experimentally tested online using a TMS320F2812 digital signal processor (DSP) to prove the effectiveness of the proposed method. In this application, only the readily available drive hardware is used without employing additional components such as analog filters, signal conditioning board, external sensors, etc. As the motor drive processing unit, the DSP is utilized both for motor control and fault detection purposes, providing instantaneous fault information. The proposed algorithm processes the measured data in real time to avoid buffering and large-size memory needed in order to enhance the practicability of this method. Due to the short-time convergence capability of the algorithm, the fault status is updated in each second. The immunity of the algorithm against non-ideal cases such as measurement offset errors and phase unbalance is theoretically and experimentally verified. Being a model-independent fault analyzer, this method can be applied to all multiphase and single-phase motors.

Measurement of instantaneous shaft speed by advanced vibration signal processing - Application to wind turbine gearbox

Abstract: Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.

A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics

Abstract: This paper provides a review of the literature on condition monitoring of a gearbox. The progress and changes over the past 30 years in failure detection techniques of rotating machinery including helicopter transmission are reviewed. Vibration Analysis techniques, indicators and parameters used in condition monitoring are arranged in a historical perspective. The use of vibration-based analysis damage detection techniques is classified and discussed in details. The capability of each technique to sense failure and damage in rotary equipments is addressed. These diagnostics techniques in gearbox condition monitoring are organized and regrouped in this review paper in a better approach so they can be easily recognized.

Application of relevance vector machine and survival probability to machine degradation assessment

Abstract: Condition monitoring (CM) of machines health or industrial components and systems that can detect, classify and predict the impending faults is critical in reducing operating and maintenance cost. Many papers have reported the valuable models and methods of prognostic systems. However, it was rarely found the papers deal with censored data, which was common in machine condition monitoring practice. This work deals with development of machine degradation assessment system that utilizes censored and complete data collected from CM routine. Relevance vector machine (RVM) is selected as intelligent system then trained by input data obtained from run-to-failure bearing data and target vectors of survival probability estimated by Kaplan-Meier (KM) and probability density function estimators. After validation process, RVM is employed to predict survival probability of individual unit of machine component. The plausibility of the proposed method is shown by applying the proposed method to bearing degradation data in predicting survival probability of individual unit.

Road condition monitoring using on-board Three-axis Accelerometer and GPS Sensor

Abstract: A study by US Federal Highway Administration has shown that road condition is an essential factor of highway quality and smooth roads will lead to more comfortable driving experience and less municipal investment. International Roughness Index (IRI) has been widely used to measure pavement smoothness because it can provide a consistent rating for different measurement tools. However, existing measuring tools based on IRI are usually very expensive. In this paper, we present a low-cost vehicle-based solution, Road Condition Monitoring with Three-axis Accelerometers and GPS Sensors (RCM-TAGPS), by using a cheap three-axis accelerometer and a GPS sensor embedded in a vehicle to monitor the road condition. We analyze the Power Spectral Density (PSD) of pavement roughness, estimate IRI, and classify the pavement roughness level into four levels according to a Chinese industry standard. Experimental results show that RCM-TAGPS can evaluate pavement roughness level correctly, even under some interference like potholes, manholes and decelerating belts, and the total cost of RCM-TAGPS in each vehicle is no more than 50 dollars, which is about 1/4400 to 1/160 of the existing system used in civil engineering and municipal engineering.