A Step Toward Fault Type and Severity Characterization in Spur Gears

Gear transmissions are widely used in industrial applications and are considered to be critical components. To date, the capabilities of gear condition indicators are controversial as some condition indicators can diagnose one type of fault at the early stages, yet cannot diagnose other types of faults. This study focused on fault detection and characterization based on vibrations in a spur gear transmission. Three different common local faults were examined: tooth face fault, broken tooth, and cracks at the tooth root. The faults were thoroughly analyzed to understand the fault manifestation in the vibration signature and to find condition indicators that are robust and sensitive to the existence and severity of the fault. The analysis was based on both experimental data and simulated signals from a well-established dynamic model of the gear system. The fault detection capability of common condition indicators, as well as newly defined condition indicators, was examined and measured using statistical distances. For each fault type, the investigated condition indicators were categorized according to their discrimination power between faulted and healthy states and the ability to rank the fault severity. It was concluded that faults that affect the involute profile throughout the tooth are easily detectable. Faults such as root cracks or chipped tooth, in which mainly the tooth stiffness is affected, are much more challenging to detect. It has been shown that while using a realistic model, the capabilities of different condition indicators can be tested, and the experiments can be replaced by simulations.

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Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Shock and Vibration ◽

10.1155/2017/8345704 ◽

2017 ◽

Vol 2017 ◽

pp. 1-22 ◽

Cited By ~ 11

Author(s):

M. Buzzoni ◽

E. Mucchi ◽

G. D’Elia ◽

G. Dalpiaz

Keyword(s):

Fault Detection ◽

Vibration Analysis ◽

Vibration Signal ◽

Vibration Signals ◽

Condition Indicators ◽

Intermediate Shaft ◽

Mode Decomposition ◽

Gear Fault ◽

Vibration Signature ◽

Localized Faults

The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the vibration signals of the synchronous gears mounted on the same shaft. The physical meaningful modes are selected by means of a criterion based on Pearson’s coefficients and the fault detection is performed by dedicated condition indicators. The proposed method is validated taking into account simulated vibrations signals and real ones.

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International Journal of Prognostics and Health Management, ISSN 2153-2648, 2017 019 1 A Condition Based Maintenance Implementation for an Automated People Mover Gearbox

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2017.v8i3.2648 ◽

2020 ◽

Vol 8 (3) ◽

Author(s):

Ali Ashasi-Sorkhabi ◽

Stanley Fong ◽

Guru Prakash ◽

Sriram Narasimhan

Keyword(s):

Fault Detection ◽

Real Time ◽

Health Management ◽

Industrial Applications ◽

Remaining Useful Life ◽

Condition Based Maintenance ◽

Vibration Data ◽

Condition Indicators ◽

Failure Data ◽

Wide Range

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.

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A hybrid hidden Markov model towards fault detection of rotating components

Journal of Vibration and Control ◽

10.1177/1077546315627934 ◽

2016 ◽

Vol 23 (19) ◽

pp. 3175-3195 ◽

Cited By ~ 8

Author(s):

Ayan Sadhu ◽

Guru Prakash ◽

Sriram Narasimhan

Keyword(s):

Fault Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Time Scale ◽

Hidden Markov ◽

Energy Operator ◽

Fault Classification ◽

Multiple Faults ◽

Condition Indicators ◽

Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

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Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

10.32920/ryerson.14651667 ◽

2021 ◽

Author(s):

Jing Yaun

Keyword(s):

Fault Detection ◽

Health Monitoring ◽

Power Efficiency ◽

Joint Torque ◽

Robot System ◽

Operation Conditions ◽

Efficiency Estimation ◽

Joint Torque Sensor ◽

Reconfigurable Robot ◽

Critical Components

Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

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Fault Detection in Bevel Gear Using Condition Indicators

Lecture Notes in Mechanical Engineering - Advances in Engineering Design ◽

10.1007/978-981-33-4018-3_38 ◽

2021 ◽

pp. 403-416

Author(s):

Mahendra Singh Raghav ◽

Rahul Tiwari ◽

Ram Bihari Sharma

Keyword(s):

Fault Detection ◽

Bevel Gear ◽

Condition Indicators

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Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

Energies ◽

10.3390/en12173279 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3279 ◽

Cited By ~ 7

Author(s):

Zuolu Wang ◽

Jie Yang ◽

Haiyang Li ◽

Dong Zhen ◽

Yuandong Xu ◽

...

Keyword(s):

Spectral Analysis ◽

Condition Monitoring ◽

Induction Motors ◽

High Reliability ◽

Industrial Applications ◽

Experimental Investigations ◽

Signature Analysis ◽

Step Size ◽

Time Service ◽

Vibration Signature

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.

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LOAD AND STRESS ANALYSIS OF CYLINDRICAL WORM GEARING USING TOOTH SLICING METHOD

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2006-0007 ◽

2006 ◽

Vol 30 (1) ◽

pp. 97-111 ◽

Cited By ~ 4

Author(s):

A.H. Falah ◽

A.H. Elkholy

Keyword(s):

Spur Gear ◽

Operating Conditions ◽

Stress Distributions ◽

Worm Gearing ◽

Tooth Stiffness ◽

Worm Gears ◽

Foundation Deformation ◽

Stiffness Variation ◽

Slicing Method

A method for the determination of load and stress distributions of the instantaneously engaged teeth of cylindrical worm gears is represented in this paper. The method is based on the assumption that both the worm and gear can be modeled as a series of spur gear slices. The exact geometry and point of load application of each slice depends on its location within the mesh. By calculating the applied load and stress for each slice, the same can be determined for the entire worm gear set. The method takes into consideration tooth stiffness variation from root to tip, tooth bending deflection, local contact deformation, tooth foundation deformation and, the influence of gear parameters on load and stress. Calculated results were found to be in agreement with experimental and analytical ones obtained from literature under given operating conditions.

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Material Based Fault Detection Methods for PV Systems

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.865.111 ◽

2020 ◽

Vol 865 ◽

pp. 111-115

Author(s):

Khaled Osmani ◽

Ahmad Haddad ◽

Thierry Lemenand ◽

Bruno Castanier ◽

Mohamad Ramadan

Keyword(s):

Fault Detection ◽

Raw Materials ◽

Raw Material ◽

Detection Methods ◽

Critical Aspect ◽

Pv System ◽

Detection Techniques ◽

Pv Systems ◽

Fault Type ◽

Pv Cell

The overall efficiency of a PV system is strongly affected by the PV cell raw materials. Since a reliable renewable energy source is expected to produce maximum power with longest lifetime and minimum errors, a critical aspect to bear in mind is the occurrence of PV faults according to raw material types. The different failure scenarios occurring in PV system, decrease its output power, reduce its life expectancy and ban the system from meeting load demands, yielding to severe consecutive blackouts. This paper aims first to present different core materials types, material based fault occurring on the PV cell level and consequently the fault detection techniques corresponding to each fault type.

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