Planetary Gearbox Fault Diagnosis Using an On-Rotor MEMS Accelerometer

Conventional accelerometers installed on housing often give out less accurate diagnostic results for planetary gearbox because the mesh excitation of planet gears change with carrier movement. Recent significant advancements in low-power and low-cost Micro-Electro-Mechanical Systems (MEMS) technologies make it possible and easier to mount MEMS accelerometers directly on the rotating shaft, enabling more accurate dynamic characteristics of the rotating machine to be acquired and used for condition monitoring. In this paper, two tiny MEMS accelerometers are installed diametrically opposite each other on the lowspeed input shaft of a planetary gearbox to measure the acceleration signals. The acceleration signals sensed by each MEMS will contain both the tangential acceleration and gravitational acceleration, but the latter can be removed by summing the acceleration signals from both sensors in order to characterise the rotor dynamics precisely. The experimental results show that the tangential acceleration measured on the low-speed input shaft of a planetary gearbox can clearly indicate faults, thus providing a reliable and lowcost method for planetary gearbox condition monitoring

INVESTIGATION OF DYNAMIC RESPONSES OF ON-ROTOR WIRELESS SENSORS FOR CONDITION MONITORING OF ROTATING MACHINES

The most common sensors that are used to monitor the condition of a machine health are wired accelerometers. The big advantages of using these types of accelerometers are their high performance and good stability. However, they have certain drawbacks as well. These accelerometers are large in size and require a cable for external power source. Hence a more reliable and cheaper alternatives of these conventional accelerometers are needed that can eliminate the drawbacks of the wired accelerometers. This thesis reports the application of wireless Micro-Electro-Mechanical System (MEMS) accelerometer for machinery condition monitoring. These sensors are so small that they can be easily mounted on the rotating machine parts and can acquire dynamic information very accurately. One critical problem in using an on-rotor accelerometer is to extract the true tangential acceleration from the MEMS outputs. In this research, the mathematical model of an on-rotor triaxial MEMS accelerometer output signals is studied, and methods to eliminate the gravitational effect projected on X-axis (tangential direction) are proposed. The true tangential acceleration that correlates to the instantaneous angular speed (IAS) is reconstructed by combining two orthogonal outputs from the sensor that also contain gravitational accelerations. To provide more accurate dynamic characteristics of the rotating machine and hence achieving high-performance monitoring, a tiny MEMS accelerometer (AX3 data logger) has been used to obtain the on-rotor acceleration data for monitoring a two-stage reciprocating compressor (RC) based on the reconstruction of instantaneous angular speed (IAS). The findings from the experiments show that the conditions of the RC can be monitored and different faults can be identified using only one on-rotor MEMS accelerometer installed on compressor’ flywheel. In addition, the data collection method is improved by considering the wireless data transmission technique which enables online condition monitoring of the compressor. Thus, a wireless MEMS accelerometer node is mounted on the RC to measure the on-rotor acceleration signals. The node allows the measured acceleration data to be streamed to a remote host computer via Bluetooth Low Energy (BLE) module. In addition, the device is miniaturised so that can be conveniently mounted on a rotating rotor and can be driven by a battery powered microcontroller. To benchmark the wireless sensor performance, an incremental optical encoder was installed on the compressor flywheel to acquire the instantaneous angular speed (IAS) signal. Furthermore, conventional accelerometer mounted on the machine’s housing provide lower accuracy in diagnosis the faults for planetary gearboxes because of the planet gears’ varying mesh excitation due to its carrier movement. In contrast, installation of the smaller AX3 MEMS accelerometers is done at diametrically opposite direction to the each other of the planetary gearbox’s low-speed input shaft, allowing measurement of the acceleration signals which are used for condition monitoring of the gearbox. The findings from the experiments demonstrate that when tangential acceleration is measured at the planetary gearbox’s low-speed input shaft, effective fault identification is possible, offering reliability and economy in monitoring the health of planetary gearboxes

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis

Orthogonal on-rotor sensing vibrations for condition monitoring of rotating machines

Thanks to the fast development of micro-electro-mechanical systems (MEMS) technologies, MEMS accelerometers show great potentialities for machine condition monitoring. To overcome the problems of a poor signal to noise ratio (SNR), complicated modulation, and high costs of vibration measurement and computation using conventional integrated electronics piezoelectric accelerometers, a triaxial MEMS accelerometer-based on-rotor sensing (ORS) technology was developed in this study. With wireless data transmission capability, the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high SNR. The orthogonal outputs lead to a construction method of analytic signals in the time domain, which is versatile in fault detection and diagnosis of rotating machines. Two case studies based on an induction motor were carried out, which demonstrated that incipient bearing defect and half-broken rotor bar can be effectively diagnosed by the proposed measurement and analysis methods. Comparatively, vibration signals from translational on-casing accelerometers are less capable of detecting such faults. This demonstrates the superiority of the ORS vibrations in fault detection of rotating machines

Requirements for Sensor Integrating Machine Elements : A Review of Wear and Vibration Characteristics of Gears

For condition monitoring of machines sensor integrating standard machine elements provide advantage in acquiring high-quality, robust data from individual machine elements and reducing effort in signal processing. However, research covering small and inexpensive consumer-grade MEMS sensors with respect to integration and measurement requirements for wear detection is limited. In order to define such requirements, the state of the art of vibration-based condition monitoring of gears is reviewed and summarised. The focus is on the characteristics of progressive wear and how it might show in the vibration signal. The review finds that correlation between wear and vibration characteristics of gears exist, but the interpretation of the vibration signals is challenging and requires purpose-built signal processing methods. The review also concludes that integrated MEMS acceleration sensors are theoretically able to measure the vibration characteristics of gears to detect wear. Important characteristics are the gear mesh acceleration with its frequencies and harmonic multiples (GMFi). Frequency range requirements for the sensors depend on the operating conditions of gears, the upper frequency limit needs to be greater or equal to 1.3 GMF$_{i,max}$. For the measuring range requirements, upper limits of 20 g RMS can be extracted within certain conditions. Data analysis requires a minimum frequency resolution which affects the size of memory needed for an integrated sensor system. However, there is a lack of research whether the sensitivity and internal noise behaviour of available MEMS sensors is good enough to measure relative changes in the vibration signals caused by wear

Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.Comment: Under review / added arXiv identifie

Artificial Neural Network Based Fault Diagnosis of a Pulley-Belt Rotating System

Rotating machines are widely used in various industrial fields. Hence, an unexpected stoppage due to, for example, bad operating conditions or manufacturing error, has safety implications along with economic considerations. In this research, a fault detection system for a pulley-belt rotating system is developed and then different faults simulated in a test rig are investigated. Vibration signal monitoring is utilized since it represents a reliable approach for fault recognition in rotating machinery. Time-domain signal analysis technique is applied to extract some indicative features, such as root mean square, kurtosis and skewness. An artificial neural network (ANN) model is developed to detect the simulated faults. However, in addition to the machine healthy condition five fault types, such as unbalance in the driving pulley, wear in the belt and pulleys misalignment, have been simulated in the test rig. Two MEMS accelerometers (ADXL335), interfaced to Arduino MEGA 2560 as a data acquisition device, are used for vibration amplitude measurement. LabVIEW, which is a graphical programming software, is utilized to develop a signal capturing, analysis and feature extraction system. The result showed the effectiveness of the developed system in detection of different fault types in the pulley-belt system

Fault detection and diagnosis of a multistage helical gearbox using magnitude and phase information from vibration signals

Vibration generated by a gearbox carries a great deal of information regarding its health condition. This research aims primarily on the detection and diagnosis of tooth defects in a multistage gearbox based on advanced vibration analysis. Time synchronised averaging (TSA) analysis is effective at removing noise but it is inefficient in implementation and in diagnosing different types of faults such as bearing defects other than gears. Conventional bispectrum (CB) can eliminate Gaussian noise while it preserves the signal’s phase information, however its overpopulated contents can still provide inaccurate information regarding to different types of gear faults. Recently developed modulation signal bispectrum (MSB) has the high potential to lead to the high accuracy of diagnostics of gearboxes as it more effectively characterises modulation signals such as gearbox vibrations. Therefore, the research takes MSB as the fundamental tool for analysing gearbox vibration signals and developing accurate diagnostic techniques. Firstly, it has realised that conventional techniques often ignore the effect of phase information in gearbox diagnostics. This thesis then focuses on developing CB and MSB based techniques for detecting and diagnosing of gearbox faults. Secondly, it has found that vibration responses from a multiple stage gearbox have high interferences between amplitude modulation (AM) and phase modulation (PM) which can be formalised from both gear faults and inherent manufacturing errors. However, the faults can induce wider bandwidth vibrations. Correspondingly, optimal component based schemes are also developed based on the use of MSB coherence results. Then the proposed MSB method allows an effective gearbox diagnosis using the signals in a narrower frequency band that is below twice the rotational frequency plus the highest meshing frequency amongst different gear transmission stages, being more suitable for wireless network condition monitoring systems. It has also found that the signals at resonance frequencies has a higher signal-to-noise ratio and more effective for obtaining accurate diagnosis. Also software encoder based TSA was found to be not robust and accurate due to the influences of noise and referencing components on obtaining a reliable phase signal for implementing TSA. Finally, the diagnostics carried out upon different fault cases using both CB and MSB have verified the proposed approaches can provide accurate diagnostic results, and with the new MSB based detector and estimator being more effective in differentiating between diffident fault locations for two local and one non-uniformly distributed tooth damages in a two stage helical gearbox

Vibration-based condition monitoring of wind turbine blades

Significant advances in wind turbine technology have increased the need for maintenance through condition monitoring. Indeed condition monitoring techniques exist and are deployed on wind turbines across Europe and America but are limited in scope. The sensors and monitoring devices used can be very expensive to deploy, further increasing costs within the wind industry. The work outlined in this thesis primarily investigates potential low-cost alternatives in the laboratory environment using vibration-based and modal testing techniques that could be used to monitor the condition of wind turbine blades. The main contributions of this thesis are: (1) the review of vibration-based condition monitoring for changing natural frequency identification; (2) the application of low-cost piezoelectric sounders with proof mass for sensing and measuring vibrations which provide information on structural health; (3) the application of low-cost miniature Micro-Electro-Mechanical Systems (MEMS) accelerometers for detecting and measuring defects in micro wind turbine blades in laboratory experiments; (4) development of an in-service calibration technique for arbitrarily positioned MEMS accelerometers on a medium-sized wind turbine blade. This allowed for easier aligning of coordinate systems and setting the accelerometer calibration values using samples taken over a period of time; (5) laboratory validation of low-cost modal analysis techniques on a medium-sized wind turbine blade; (6) mimicked ice-loading and laboratory measurement of vibration characteristics using MEMS accelerometers on a real wind turbine blade and (7) conceptualisation and systems design of a novel embedded monitoring system that can be installed at manufacture, is self-powered, has signal processing capability and can operate remotely. By applying the conclusions of this work, which demonstrates that low-cost consumer electronics specifically MEMS accelerometers can measure the vibration characteristics of wind turbine blades, the implementation and deployment of these devices can contribute towards reducing the rising costs of condition monitoring within the wind industry