Development of smart linear velocity measuring device by embedding sensors with the arduino microcontroller (original) (raw)
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An effective method for monitoring the vibration data of bearings to diagnose and minimize defects
MATEC Web of Conferences, 2018
Monitoring of vibration in machine tools is becoming a very important application in industry to reduce machine failures, maintenance costs, and dead time. In this paper, we propose a method to identify possible faults based on vibration data from which predictions about the working condition of the machine tools can be made. We used an accelerometer to collect the vibration data from which to analyse the health of machine tools by diagnosing whether they are in good or faulty condition for working. In our experiments, we introduced a machine called the Reliance Electric motor, which has a bearing running inside it. Our research analyses vibration data from components of the bearing including the outer bearing, inner bearing, and rolling element. The experimental results show that our method is highly accurate in diagnosing failures and significantly reduces the maintenance costs of machine tools.
Data Acquisition System and Signal Processing Technique for Bearing Fault Analysis
Indian Journal of Science and Technology, 2016
Background/Objectives: In rotating machinery bearings plays an important role to reduce rotational friction and support radial and axial loads. Abnormal vibration affects the lifetime of machinery. This abnormality may causes by bearing Fault. In order to monitor the machines health and it is needed to focus the functionality of bearing components like ball, inner race, outer race and cage component. Methods/Statistical Analysis: There are many methodologies available to measure and analyze the fault of bearing but they utilize expensive resource to identify the bearing system. The proposed system explains and utilizes a cost effective, less time consuming system, is used for bearing faults identification. The novel system provides real-time data acquisition and signal analysis for Fault detection. Low cost Micro Electro Mechanical System (MEMS) Accelerometer sensor is used for vibration measurement, LABJACK U3 Data acquisition system connected with Raspberry Pi-2 CPU which acquires and process with high performance of acquired data by using python application software. Application/Improvements: The online signal processing technique explains the abnormality of bearing time domain data's amplitude easily classify the faulty bearing. Fast Fourier Algorithm converts time domain to frequency domain which easily represents faulty components frequency with high amplitude. Short Time FFT (STFT) shows the color map with high color intensity of vibration amplitude of faulty components and its time instant. These data representation technique is utilized to easily identify fault of bearing components.
Requirements for embedded analysis concept of bearing condition monitoring
Rotating machinery maintenance cost is a signifi- cant part of total expenses of industrial plants. Current needs to reduce such expenses has risen the importance of condi- tion monitoring. Condition monitoring and diagnostics allow the maintenance that is based on current condition instead of the maintenance based on statistically estimated lifetime. If prognostic estimation is also a part of the condition monitoring system, the maintenance task can be scheduled in advance to order the necessary parts, and to reduce the overall expenses. Our research deals with a vibration-based bearing condition monitoring. This paper presents the requirements for embedded analysis concept in an industrial infrastructure, i.e. the condition monitoring system where analysis and diagnosis are done locally by embedded sensor at the field level. The requirements discussed in this paper are based on the results obtained from our current research. We also present a DSP based testing platform that has be...
Vibration Monitoring of Rotating Machines Using MEMS Accelerometer
Heavy industries face major problems since different types of mechanical failures can originate in rotating machines. Analytical approaches have demonstrated that vibration monitoring has tremendous potential in detecting and localizing defects in the machines. There are different technologies available for vibration sensing. Though MEMS accelerometer is slowly becoming an alternate method for vibration monitoring of rotating machines, yet it has not been fully explored for a much wider application base. This paper proposes the basic design for the development of a low cost MEMS accelerometer based vibration sensor by integrating the basic sensor and intelligence of vibration analysis, together. This module can easily be deployed for different rotating machines for vibration monitoring. Sensitivity of the sensor, effectiveness of the proposed intelligence in signal processing and their performance are tested for a 7.5KW, 3φ, 440V, 4 pole squirrel-cage induction motor. The experiments are carried out to check the ability to detect the fault frequency peaks under different fault combinations. The results presented here are found to be highly promising.
Review on Condition Monitoring of Bearings using vibration analysis techniques
IOP Conference Series: Materials Science and Engineering, 2018
A Bearing is one of the important components in the Rotary machines and has been widely used in various industries in many of the applications such as shaft mountings, to reduce friction as well as facilitate relative motion between the two components etc. It is therefore very essential to determine the early faults conditions from bearings. There are various methods to detect faults in the bearings, such as vibration monitoring, wear debris monitoring, temperature monitoring, soap techniques, non destructive test etc. Vibration signal analysis may be one of the commonly used techniques for checking the condition and finding faults in bearings. Vibration analysis has been used as a predictive maintenance procedure in the machine maintenance. By adopting appropriate signal processing techniques, changes in vibration signals due to faults can be detected to aid in maintaining the bearings health condition. By detecting and analyzing the machine vibration, it is possible to determine and predict the machine failure. Early fault detection of the bearings is possible by analyzing the vibration signal using different techniques. This paper give a relative of various techniques used for finding the fault in the bearings based on vibration analysis method.
Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis
Engineering and Technology Journal
Fabricate test rig to simulate the state and capture information Extraction time domain signal. Transform time domain to frequency domain by FFT transform using sigview program. Analysis result. Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor's current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.
Diagnostics of Bearing Defects Using Vibration Signal
International Journal of Computer and Electrical Engineering, 2012
Current trend toward industrial automation requires the replacement of supervision and monitoring roles traditionally performed by humans with artificial intelligent systems. This paper studies the use of hybrid intelligent system in Diagnosis of rotating machinery bearing defects. Vibration signals were collected for normal and various faulty conditions of the ball/roller bearing of the machinery. The acquired signal was processed with FFT and PSD in MATLAB to obtain the characteristic amplitudes from the frequency domain spectra of the signals. The obtained amplitude vector was used to train an adaptive neurofuzzy inference system (ANFIS) to classify and recognize normal and different faulty states. The system was tested, checked and validated with different sets of signal data. The validation data attests to the structural stability and performance of the system.
A smart sensor for the condition monitoring of industrial rotating machinery
Proceedings of IEEE Sensors, 2012
A multi-variable smart sensor is presented able to gather, validate, and locally process data on both temperature and tri-axial vibration, as part of an instrumentation system which, integrating small, smart, and deeply embedded 'field' devices in large numbers, may address the data requirements placed by new sophisticated Predictive Maintenance criteria applied to industrial rotating machinery.
IEEE Xplore, 2012
The real-time monitoring of events in an industrial plant is vital, to monitor the actual conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes condition monitoring of the rotating machinery, to anticipate possible conditions of failure. To increase the operational reliability it is thus necessary an efficient tool to analyze and monitor the equipments, in real-time, and enabling the detection of e.g. incipient faults in bearings. To fulfill these requirements some innovations have become frequent, namely the inclusion of vibration sensors or stator current sensors. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades, and replacement of the monitored machine, as well as expansion of the monitoring system. This paper presents the development, implementation and testing of an instrument for vibration monitoring, as a possible solution to embed in industrial environment. The digital control system is based on an FPGA, and its configuration with an open hardware design tool is described. Special focus is given to the area of fault detection in rolling bearings.
MEMS Accelerometers: Testing and Practical Approach for Smart Sensing and Machinery Diagnostics
Microsystems and Nanosystems, 2016
Wireless sensor networks with on-board signal processing capabilities are becoming very attractive for machinery condition monitoring purposes. Their advantages in cost and size are becoming an important factor for deployment in a new generation of maintenance-e-maintenance. In this paper, an intelligent monitoring system for e-maintenance (IMSEM) is presented using a recently developed and tested MEMS accelerometer, a low-power microprocessor and a wireless communication module. The system has a compatible framework and interface with Open Systems Architecture for Condition-Based Maintenance (OSA-CBM). Importantly, it integrates OSA-CBM-defined functions, including sensing module, signal processing, condition monitoring and health assessment; thus, the developed system can successfully reducethe monitoring complexity and communication overhead with human operators. The performance of IMSEM is evaluated by carrying out fault diagnostic on rotating unbalance of a mechanical shaft driven by a DC motor with varying loads and speeds. By analysing the signals from the vibration signal and rotating speed, the IMSEM has the ability to carry out on-board fault detection and diagnosis. The IMSEM achieved an accuracy of 95% in detecting the fault and severity of rotor unbalance, which is based on the ISO 1940-1:2003.