جستجوی مقالات مرتبط با کلیدواژه « پایش وضعیت » در نشریات گروه « مکانیک »

Demand for cost-effective, reliable operation and safety of machinery, especially reciprocating compressors, which are among the most expensive machines in maintenance, requires accurate troubleshooting and fault classification. Due to their advantages, data-driven methods are often preferred to physical modeling methods for fault detection. This research simulates the mathematical model of a two-stage reciprocating compressor and conventional faults for use as a monitored system. The artificial neural network used is the probabilistic neural network whose main task is classification. Classification classes include one healthy compressor class and seven defective compressor classes for eight classes. Classification with the probabilistic neural network was performed using time domain and envelope spectrum characteristics. Then, the selected features are optimized using a genetic algorithm before feeding into the probabilistic neural network. Classification with the probabilistic neural network using time-domain characteristics shows a poor classification percentage with 44% Correct accuracy. But classification with a probabilistic neural network and envelope spectrum features has a 95% correct classification accuracy. Also, optimizing the selection of statistical features of the time-domain and frequency envelope spectrum with a genetic algorithm brings 48 and 99% correct accuracy in classification, respectively.

In the existing maintenance and repair system in industries, there are different strategies that in every industry, a specific strategy is tried to be implemented as the best strategy. What is certain is that trying to implement a specific strategy in the product maintenance and repair system is a closed view and it is necessary to select and implement the desired strategies according to the situation of each industry. As an example and in particular, the predictive maintenance strategy should be operational along with the preventive strategy to make the way to achieve the organizational goals of maintenance and repairs smoother. In some cases, it is even necessary to resort to a work-until-failure strategy for some equipment. Implementing a world-class condition monitoring program should be a condition monitoring unit's first priority. This article describes three case studies of the achievements of the simultaneous application of different condition monitoring techniques in the troubleshooting of rotating equipment. Increasing the accuracy of reports of the condition monitoring unit leads to a reduction in the time and cost of repairs and increases the satisfaction and trust of the management towards the condition monitoring unit. The simultaneous use of condition monitoring techniques is the best way to avoid providing incorrect, multifaceted and misleading reports, which sometimes lead to mistrust of different units and the management of a production complex regarding the category of condition monitoring.

The purpose of condition monitoring is to monitor the conditions of an asset in order to predict its failure. The first step in implementing condition monitoring is to establish a health indicator. To construct the health indicator, operational data such as vibrational data should be collected from the asset during its operation, followed by extracting meaningful features from the data.This study introduces an artificial intelligence model (convolutional autoencoder) for feature extraction from the vibration data, which only requires healthy status data for training. For this purpose, the vibration data of the asset is gathered online during operation in a healthy state to establish the healthy dataset. Then, the deep learning model is trained by the healthy dataset. Finally, after the failure stage is detected, the health indicator is established by measuring the differences in the vibrations of the healthy and the failure conditions. The performance of the proposed model is evaluated by vibrational data of a gearbox. The health indicator exhibits a monotonically increasing degradation trend and has good performance in terms of detecting incipient faults.

Lobe compressors are one of the most important compressors used in the steel industry, and the occurrence of any failure in this equipment leads to production stoppage and imposing heavy losses on steelmaking units. Neglecting to monitor the precise condition of lobe compressors has led to the growth of the vibration range of the compressor as well as the adjacent equipment and piping, and in addition to creating misalignment between the compressor, gearbox and electric motor, it has led to the failure of bearings and the failure of precision instrument connections and branching of piping lines upstream. And it will be downstream of the compressors.In addition to the negative consequences of increasing the cost of repairs and reducing production, this issue will cause risks such as process gas leakage. In this article, the study and solution of the vibration problem of the mentioned equipment in one of the country's steelmaking complexes has been discussed.

Detection of rotor asymmetry faults (RAFs) in induction machines (IMs) based on stator current signature of machine due to the presence of low-frequency load torque oscillation (LTOs) can cause false alarm (FM). Therefore, isolating the RAFs from the LTOs can improve the condition-based monitoring system. The methods discussed in the past generally require three-phase current and voltage of stator windings information along with machine angular velocity. In this paper, a new method based on single phase machine data is presented. In this regard, a virtual rotating reference frame based on Hilbert transform is provided which do not need the angular velocity of machine. In order to improve the output spectrum resolution and low computational cost, the proposed method is combined with the Gortzel algorithm. The proposed method is tested and evaluated by synthetic data and then evaluated by means of experimental results. The results show that this method can isolate the RAFs indices from LTOs, effectively.

In this study, a new method for the fault detection of the locomotive engine injector nozzle based on vibration analysis and statistical tests, inside artificial neural networks, is presented. For this point, first the under study received vibration signals in the frequency domain is divided into several smaller ranges and the RMS of each range is then extracted as a frequency property and given as an input to the neural network. Because the high selection of the features reduces the accuracy of the neural network, the extracted feature vector with different levels of significance passes through the T-test filters, firstly, and then enters the neural network as an input. Using of this method, the accuracy of the neural network increases from 78.4 to 94.6%, and also help to detect the frequency ranges. According to the results, the fault of the injector nozzle crack increases the intensity of vibrations in the upper band frequencies of 1500 Hz.

In recent years, with the advent of the Fourth Industrial Revolution concepts and the development of artificial intelligence technologies, new approaches such as the digital twin have been introduced. In a digital twin, a virtual counterpart of the physical system during its whole life is created, with abilities such as analyzing, evaluating, optimizing, and predicting. The first step in creating a digital twin model is to construct a (multi) digital health indicator that describes different aspects of the physical component state during the whole life of the component. In this research, a new method for constructing health indicators based on vibration measurement and a deep learning model has been introduced. For this purpose, the Continuous Wavelet Transform was used to convert the raw vibration signals into two-dimension images; Then, the deep learning model was used to extract features from the images and the health indicator is constructed based on the differences of the images in normal and failure stages. In this article, various Autoencoder architectures are discussed, and it is demonstrated that the Convolutional Autoencoder has better performance in terms of detecting incipient faults. The performance of the proposed model is evaluated by the vibration data of the bearing, and the constructed health indicator exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

Defects in adhesive joints are an important issue in the construction of space structures. In this paper, using lamp waves, suitable properties have been obtained to identify the size and position of the defects of the adhesive joints. Using finite element simulations, the effect of the defect on the propagation of the lamp waves has been investigated. Simulations have been performed for three different adhesive thicknesses, three different sizes of circular defects in 9 different positions, and the effect of each of them on the wave passing through the joint has been investigated. The signals obtained from the faulty connections were compared with the signal obtained from the healthy connection and the desired area was isolated from the total received signal for further analysis. The proper and correct separation of defects requires finding suitable characteristics for it. Therefore, 34 features were examined to differentiate and separate defects. Then, the neural network was used to provide the basis for creating appropriate patterns for the separation of defects. The percentage of correct detection of neural network for adhesive thickness separation was 93.8%, for defect area separation in terms of size 100% and for defect position separation in X and Y axes were 96.1 and 95.1%, respectively. The obtained results show the efficiency of the improved distance evolution method and the features selected to distinguish the defects of such connections.

Bearings are one of the main components used in the drive-train of electrical machines. Early fault diagnosis and classification of bearing fault for maintenance of electromechanical system are very important. With progresses in measurement and digital systems, extensive range of real-time data can be available in electrical machines. Since fault diagnosis based on signal processing methods from extracted signals may not be possible due to different reasons such as noise level, natural frequencies of system, saturation of core,, severity of fault and load torque, deep learning methods have been considered in recent years. In this paper, time series deep learning method for condition monitoring of bearing in electrical machine for the purpose of detection and classification of fault is considered. Obtained results by means of proposed method have been compared with pervious techniques. Experimental results show that proposed deep learning method can detect and classify bearing fault with accuracy above 90%.

In the maintenance system in the industry, there are different strategies that are tried in each industry.Implement a specific strategy as the top strategy.Attempt to implement a specific strategy in product maintenance and repair system is a closed view and it is necessary to suit the situation of each industry, the desired strategies select and implement.For example, a predictive repair strategy should be combined with an operational preventive strategy to pave the way for organizational maintenance goals. In some cases it is even necessary for some equipment resorted to work strategy until failure.This paper describes a case study of the achievements of simultaneous implementation of condition monitoring techniques including vibrations and acoustic emissions. The effect of water pollution on bearing life is also investigated has taken. In the following, while examining the observed failure, to find the root causes of the failure and provide targeted solutions Similar failures have been prevented.

Since the maintenance and repairing costs of mechanical systems, such as structures and rotating machines are significantly high, one way to reduce these costs is to consider some approaches before any operational work to check for damages in such systems. In this study, a new method is presented for damage detection of offshore jacket structures in the presence of various uncertainties, such as modeling errors, measurement errors and environmental noises, based on the simulated model and intact state of the real model. In the proposed method, real intact structure data is used to update the simulated model parameters. Some parts of the signal that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. Frequency data is extracted from the vibrational signals using the frequency domain decomposition method. A deep auto-encoder neural network is designed to learn the damage-sensitive features from the frequency data and to damage detection of the structure. In order to train the proposed deep network, frequency data of the simulated model and real intact state are used; then the frequency data of the real structure is used to test the proposed deep network. The results show that the proposed method is capable for damage detection of the offshore jacket structure with more accurate results than the other comparative methods.

Early fault detection of the rolling element bearings has a very important role in increasing the reliability of rotating machines.It leads to better decision-making for maintenance activities.  In recent decades, the shock pulse method has been developed to detect faults in the early stage of rolling element bearings degradation. In this paper, the accuracy of the remaining useful life estimation using extracted features from vibration signals and that from the shock pulse method are compared. In this regard, a set of accelerated life tests on rolling element bearings were designed and performed. Both shock pulse signals and vibration signals of the under-test rolling element bearings were recorded. Then two models based on feed-forward neural-network are developed to predict the remaining useful life of rolling element bearings. In the first model, only extracted features from vibration signals are fed for remaining useful life prediction. In the second model, the extracted features from shock pulse method are fed too. The results show that using shock pulse method-based features improves the accuracy of remaining useful life estimation. Also, using the health indicators extracted from vibration analysis and shock pulse method leads to a better estimating of the degradation behavior.

Condition monitoring of mechanical systems, such as structures and rotating machines is always a major challenge. This paper is presented a new method for damage detection of real mechanical systems in presence of the uncertainties such as modeling errors, measurement errors, varying loading conditions, and environmental noises based on a simulated model and real healthy state. In this method, data of a real healthy system is used to updating the parameters of the simulated model. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. A deep convolutional network is designed to learn damage-sensitive features from raw frequency data of simulated model and real healthy state. Raw frequency data is extracted from vibration signals using the power spectral density method. In order to train the proposed deep network, raw frequency data of the simulated model andreal healthy state are used. Then, raw frequency data of the real model are used to test the proposed deep network. The proposed method is validated using an experimental beam structure. The results show that using the proposed algorithm for identification and damage detection of the beam-like structure has more accuracy with respect to the other comparative methods

In the maintenance system in industries, there are different strategies that in each industry, a specific strategy is tried to be implemented as a superior strategy.What is certain is that trying to implement a specific strategy in the product maintenance system is a closed view and it is necessary to select and implement the desired strategies according to the situation of each industry.In order to properly implement the situation monitoring techniques, it is necessary to implement each technique in a principled manner and compare the results obtained from each technique with the output results of other techniques. In some cases, a particular technique may be effective in diagnostics, but in most cases it can be said that the combination of different techniques helps to make the right decision.In the present study, while explaining how to use different situation monitoring techniques, the achievements of the simultaneous implementation of these techniques in one of the country's oil fields have been discussed.

Flanged joints’ looseness is among the common causes for the failure of industrial structures with flanged joints the timely detection of which can prevent the imposition of heavy financial losses and in some cases injuries. There are conventional methods for detecting this fault and each of them has its own drawbacks. For instance, torque control methods have high error of measurement, impedance-based measurement methods, have high expenses, and vibration or ultrasonic methods lack accuracy due to the use of linear phenomena in fault detection. Vibro-Acoustic modulation method is one of the nonlinear fault detection methods that detect and assess looseness of flanged joints with high precision through the measurement of the intensity of the vibrational and ultrasonic signals modulation applied to the structure. This paper was an attempt to investigate the efficiency of the Vibro-Acoustic modulation method in the detection and evaluation of flanged joints numerically and experimentally. For this, a laboratory sample composed of two pipes connected with flanged joints is employed. It is revealed that this method can detect bolt looseness with 12.5% precision. In addition, the effect of parameters such as ultrasonic and vibrational frequency, amplitude of applied torque, sensors and actuators position, as well as excessive increase of torque have been examined and the model precision in the studied structure has been estimated.

Adhesive joints have been employing in engineering structures such as marine, aerospace, automotive, oil and gas industry. Since the joints are the weakest part of engineering structures, the fracture possibility in adhesive joint is high. Therefore, structural health monitoring of adhesive joint is an important issue. The aim of this paper was condition monitoring of damage in the single lap adhesive joint by multiwall carbon nanotubes. This work is carried out by recording the electrical resistance change during the mechanical loading. This damage propagation is observed as a crack extension in adhesive joint. Firstly, MWCNTs with 9 wt.% are dispersed in an epoxy resin by ultrasonic device. Then, the hardener is added to the nonoadhesive. The obtained material is immediately poured into single lap adhesive joint mold. The defective adhesive joints were manufactured with circular and square defects in different sizes i.e. 10%, 30% and 70% overlap area. The specimen is subjected to tensile test and electrical resistance change are recorded during the shear load. The results showed that the maximum value of relative resistance change is occurring in the adhesive joint comprises of square defect with 30% overlap area of defect. In this situation, the crack was propagated in nanoadhesive and a small part of a crack was extended from defect boundary.

Reduce the cost of unscheduled shutdown and enhance the reliability of systems, is one of the important goals for various industries that could be achieved by condition monitoring. Cavitation is a common phenomenon in centrifugal pumps which causes the damage and its true identification in early stage is too important and help to increase the life of pump. In this paper cavitation is identified by use of Artificial Immune Net (AIN) that is modeled on the function of the human immune system. For this purpose, first data collection were done by a laboratory setup and various features extracted form vibration and current signals, in next step, feature selection and dimensions reduction were done by artificial immune method, then with AIN method, the system condition was identified. Finally, for comparing the results of this study with other methods, first feature selection and dimensions reduction were down by the PCA method then fault detection were down by nonlinear supportive vector machine (SVM), multi-layer artificial neural network (MLP), K-means and FCM methods with features of PCA and AIN methods. The results shown that AIN methods have more precise than other methods.

In this study, an example of the results obtained from the combination of the vibration monitoring program and the root cause analysis approach for the electromotor roller element bearings of the cement factory’s mill fan has been presented and examined. By registering the inspectors' reports on the release of abnormal sound from the bearings, the vibration data recorded in the monitoring program were equipped and, by carefully checking the vibration trends of the machine, sensible increase in the bearing condition index (BC) have seen. By matching the fault frequency with the frequency elements of the roller bearing, predicted is failure in the bearing' cage, which will be verified by visited and reviewed. The detect of the root cause of the failure is on the agenda for this purpose, paid investigated the causes of failure in the bearings and due to the inspection history, finally specified the use of the bearing is not suitable due to the velocity factor, as well as the factors of the lubrication interval and the amount of lubrication charged can be explained by the reasons for failure in the machine.

Currently, most of the available tool wear condition monitoring systems are based on the signal features quantities that are correlated with tool wear. The evaluation of tool wear based on one sensor is not reliable because the measured features depends not only on tool wear but also on other process parameters and random disturbances. For solving this problem, multi sensor data fusion is used to combine disparate sensory data. The obtained information are more accurate and reliable. In this research, combination of vision, current, strain and vibration sensors for predicting flank wear land is proposed. An optimized adaptive neuro-fuzzy inference system (ANFIS) model is developed to fuse the surface image, motor current, strain and vibration signal features. The structure of proposed ANFIS model has four inputs and one output. The inputs of the model are entropy of surface image (which is filtered by undecimated wavelet transform), time-frequency marginal integral of the motor current, Shannon entropy of strain and Shannon entropy of vibration signals, while output of the model is the flank wear. The results showed that the optimized ANFIS model can be used to fuse the signal features and predict tool flank wear with high accuracy.

Oil analysis is one of the main methods for monitoring the status of internal combustion engines and oils to ensure the lubricant's protective performance and engine health. This paper investigate oil condition monitoring in gasoline engine using oil dielectric coefficient measurement. At first capacitive sensor pectinate type is designed and manufactured that engine oil can pass through the sensor with high resolution and accuracy for engine monitoring application. Results show that up to 1.5% water pollution in oil, dielectric coefficient and θ is increased linearly. More water pollution increase dielectric coefficient exponentially. Due to investigate oil oxidation in controllable real condition, this sensor and another industrial quality sensor is evaluated in engine durability test. Although oil dielectric coefficient is increased with oil oxidation but also θ is constant permanently. Monitoring the result of dielectric behavior of engine oil in new design sensor is similar to industrial quality sensor. In addition interaction effect of water pollution and oil oxidation is studied. Results indicate that this sensor is capable of detecting water pollution and oxidation in engine oil.