Modal analysis and condition monitoring for an electric motor through MEMS accelerometers

Piezoelectric accelerometers are commonly employed for diagnosing machine faults, due to their accuracy. In the last few years, however, MEMS (Micro Electro-Mechanical Systems) accelerometers have attracted strong interest thanks to their low cost. In this work, a synchronous electric motor with an integrated MEMS sensor is studied and results are compared from both MEMS and piezoelectric sensors. A modal analysis is performed, using data from all available sensors. Comparing the frequency response functions and the natural frequencies shows the limitations of the MEMS sensor. One can then correct the MEMS measurements, by using global statistical parameters calculated on the data or by defining a “filter” function between the signals, thus improving the signal-to-noise ratio. It is found that MEMS sensors may replace piezoelectric ones for diagnostic applications. This way, an inexpensive measurement system (which needs to be calibrated only once, before installation, against higher-accuracy sensors) can be used for vibration monitoring of electric motors

The use of passive telemetry in rotor fault diagnosis

The sensors most commonly used for monitoring machine health are wired accelerometers because of their high performances and good stability. However, these transducers are usually large in size; require an external power source. Hence, there is a need for cheaper and reliable alternative for the conventional accelerometers. This thesis reports the development of a wireless accelerometer based on Micro-Electro-Mechanical System (MEMS) inertial sensor and off-the-shelf digital RF communication modules. It is small enough to be installed on the rotating shaft of a machine. In addition, it has a high enough resolution to be used to analyse the dynamic behaviour of rotating shaft. The wireless sensor is mounted with its sensitive axis in the tangential direction with respect to the centre of the rotor. This position allows the sensor to perform high resolution tangential acceleration measurements and nullifies the centripetal acceleration. To assist in the validation of the wireless sensor, a mathematical model was derived to simulate the vibration signals from the test rig. Experimental and simulated results both confirmed the effectiveness of the wireless sensor in detecting different degrees of misalignments and unbalance of a flexible rotor system. The wireless sensor has been confirmed to possess the capability of detecting small degrees of misalignment using the spectral amplitude of the peak at 2X running speed compared to other conventional sensors (wired accelerometers, laser vibrometers). In addition, the results of the experiment and simulation have also confirmed the capacity of the wireless sensor to detect different shaft unbalance grades at 1X running speed using spectral and order magnitudes. However, the wired sensors used for comparison failed to show any clear separation of the different grades of shaft unbalance. Moreover, it has been observed that the instantaneous angular speed (IAS) derived directly from the wireless sensor correlates well with that obtained from a shaft encoder and showed the capacity to detect the main features of rotor dynamics. An advanced algorithm has been developed to remove the gravity effect. The application of the algorithm has made the IAS computed from the wireless sensor more indicative to that obtained by a shaft encoder

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

An autonomous and intelligent system for rotating machinery diagnostics

Rotating machinery diagnostics (RMD) is a process of evaluating the condition of their components by acquiring a number of measurements and extracting condition related information using signal processing algorithms. A reliable RMD system is fundamental for condition based maintenance programmes to reduce maintenance cost and risk. It must be able to detect any abnormalities at early stages to allow preventing severe performance degradation, avoid economic losses and/or catastrophic failures. A conventional RMD system consists of sensing elements (transducers) and data acquisition system with a compliant software package. Such system is bulky and costly in practical deployment. The recent advancement in micro-scaled electronics have enabled wide spectrum of system design and capabilities at embedded scale. Micro electromechanical system (MEMS) based sensing technologies offer significant savings in terms of system’s price and size. Microcontroller units with embedded computation and sensing interface have enabled system-on-chip design of RMD system within a single sensing node. This research aims at exploiting this growth of microelectronics science to develop a remote and intelligent system to aid maintenance procedures. System’s operation is independent from central processing platform or operator’s analysis. Features include on-board time domain based statistical parameters calculations, frequency domain analysis techniques and a time controlled monitoring tasks within the limitations of its energy budget. A working prototype is developed to test the concept of the research. Two experimental testbeds are used to validate the performance of developed system: DC motor with rotor unbalance and 1.1kW induction motor with phase imbalance. By establishing a classification model with several training samples, the developed system achieved an accuracy of 93% in detecting quantified seeded faults while consumes minimum power at 16.8mW. The performance of developed system demonstrates its strong potential for full industry deployment and compliance

A tutorial review on time-frequency analysis of non-stationary vibration signals with nonlinear dynamics applications

Time-frequency analysis (TFA) for mechanical vibrations in non-stationary operations is the main subject of this article, concisely written to be an introducing tutorial comparing different time-frequency techniques for non-stationary signals. The theory was carefully exposed and complemented with sample applications on mechanical vibrations and nonlinear dynamics. A particular phenomenon that is also observed in non-stationary systems is the Sommerfeld effect, which occurs due to the interaction between a non-ideal energy source and a mechanical system. An application through TFA for the characterization of the Sommerfeld effect is presented. The techniques presented in this article are applied in synthetic and experimental signals of mechanical systems, but the techniques presented can also be used in the most diverse applications and also in the numerical solution of differential equation

Exploration of a Condition Monitoring System for Rolling Bearing Based on a Wireless Sensor Network

In recent years, wireless sensor networks (WSN) have attracted attention in machine condition monitoring (CM) fields for a more efficient system based on the inherent advantages of WSN, including ease of installation and relocation, lower maintenance cost and the ability to be installed in places not easily accessible. As critical components of rotating machines, bearings account for more than 40% of the various types of failures, causing considerable unpredicted breakdowns of a plant. Thus, this thesis intends to develop a cost-effective and reliable wireless measurement system for rolling bearing condition monitoring. Based on the investigation of various wireless protocols, Zigbee has been taken as a the most promising candidate for establishing the wireless condition monitoring system as it can have an acceptable bandwidth at low power consumption. However, a comparison made between wired and wireless measurement system has found that the Zigbee based wireless measurement system is deficient in streaming long continuous data of raw vibration signals from typical application environment with inevitable ambient interference. As a result, data loss can happen from time to time. To solve this issue, an on-board processing scheme is proposed by implementing advanced signal processing algorithms on the sensor side and only transmitting the processed results with a much smaller data size via the wireless sensor network. On this basis, a wireless sensor node prototype based on the state-of-the-art Cortex-M4F is designed to embed customizable signal processing algorithms. As an extensively employed algorithm for bearing fault diagnosis, envelope analysis is chosen as the on-board signal processing algorithm. Therefore, the procedure of envelope analysis and considerations for implementing it on a memory limited embedded processor are discussed in detail. With the optimization, an automatic data acquisition mechanism is achieved, which combines Timer, ADC and DMA to reduce the interference of CPU and thus to improve the efficiency for intensive computation. A 2048-point envelope analysis in single floating point format is realized on the processor with only 32kB memory. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data; this allows a large number of sensor nodes to be deployed in the network for real time monitoring. Furthermore, a computation efficient amplitude based optimal band selection algorithm is proposed for choosing an optimal band-pass filter for envelope analysis. Requiring only a small number of arithmetical operations, it can be embedded on the wireless sensor node to yield the desired performance of bearing fault detection and diagnosis