After multiple attempts, I was able to go online successfully using USB to RS485 adapter. But, the new drive shows up as an additional device. My efforts to download the pre-configured offline drive parameters into the target device was in vain. What steps are necessary to tell it the target drive is actually one of the pre-configured device listed in the project?
I will try your suggestion. But just wanted to be clear. The drive will be connected through profibus during normal operation but I have to take out the profibus module to hook up the USB to RS485 adapter. Does the new drive still need to have the same profibus address as the offline program in order for the download to work?
Starter 41 Siemens Download
Just follow the Uploading and downloading drive parameters of a MICROMASTER 4xx using STARTER application example & download project in target drive, then change node address & connect profibus module.
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Motor and starter protection is taken to a new level by combining a high-end motor protection relay with a heavy duty solid state starter. Flexible control features and selectable ramping profiles to match any application... no need to compromise performance. High level circuit isolation via fiber optics (standard on all units) for safety and power quality immunity. Sealed NEMA 12 enclosures are standard equipment, not an expensive option. Soft start & protect any AC motor.
The MVC4 Series starter is designed to start AC motors in any fixed speed application. It provides maximum protection with "True Thermal Modeling," while allowing smooth, stepless control of acceleration and deceleration. The MVC Plus Series guarantees power control and protection for your most important assets.
Heavy-duty attitude highest rated power devices for maximum current carrying capacity. Rated at 500% for 60 seconds, the MVC4 Series starter will never be the limiting factor in your application. Powerful sustained gate pulse insures reliable SCR firing without reactors (unlike "wimpy" pulse train designs that require a reactor to prevent SCR and motor damage) .
The evaluation license is completely free of charge and allows you to try the software to evaluate its efficiency and ease of use. You can choose either a 14-days trial version or a size-limited version. When you click download, you will be asked to register to get your license.
Attending to all the above-mentioned considerations, the automatic methods for rotor fault detection in soft-started induction motors are still improvable. This work presents a new methodology for the automatic detection and severity categorization of rotor faults in induction motors driven by soft starters. The novelty of the proposed methodology is the use of the Persistence Spectrum (PS) applied to the start-up transient stray-flux signals. Then, a convolutional neural network (CNN) is used to automatically categorize the severity of the rotor faults. In order to improve the dataset, data augmentation techniques are used. In this regard, Data Augmentation Techniques (DAT) have been proven to be a reliable way to enhance the data base used in CNN. In [18,27] it was stated that the use of Data Augmentation Techniques is a reliable method to deal with the scarcity of samples, providing a good dataset to use in a CNN. In particular, adding Gaussian noise to a signal is one of the DAT that is commonly employed.
In order to verify the effectiveness of the proposed methodology, a test-bench consisting of a 1.1 kW induction motor and a DC motor acting as a load was used. Four different commercial soft starters were used to start the motor. The obtained results, achieving an accuracy rate of 100% for each model separately and 99.89% for all the models together, show the capabilities of the proposed approach.
To carry out all the tests, four different models of soft starters were employed. Each of them had different topologies, controlling one-, two- or the three-supply phases depending on the model. Furthermore, each model allowed one to control the start-up time-ramp and the initial voltage or torque. The different models of soft starters used for the tests were the ones shown in Figure 11, and their main characteristics are listed in Table 7.
Models of tested soft starters: (a) Schneider ALTISTART 01; (b) ABB PSR3-600-70; (c) Siemens SIRIUS 3RW3013-1BB14; (d) Omron G3J-S405BL.
The tests were carried out following the same sequence for the four models of soft starters. First, the healthy motor was started, without load, by means of one of the soft starters. Different combinations of time-ramp and initial voltage/torque were performed for each model of soft starter and for each of those combinations, the tested motor was started once. Then, the same tests were repeated, but this time with the tested motor fully loaded. This was achieved by varying the excitation voltage of the DC machine coupled to the tested motor. Afterwards, the procedure was repeated first for the case of one broken bar and then for the case of two broken bars.
Although many analyses were carried out in this work, only the most representative are shown here. In this regard, in Figure 13, persistence spectra for each rotor health state and soft starter model are compared. Those persistence spectra correspond to tests when the motor was fully loaded. The settings for each soft starter model, were those corresponding to the combination of longest time-ramp and lowest initial voltage (see Table 8).
With regards to the effectiveness of the proposed methodology, Figure 15, Figure 16, Figure 17 and Figure 18 show the confusion matrices and the training progresses for each model of soft starter, separately.
Finally, in Figure 19, the confusion matrix and the training progress for all the models of soft starters combined are shown. In this case, 2835 training samples (945 samples per category) were used to train the CNN and 945 (315 samples per category) different samples were used for the validation. Although four different topologies of soft starter and different combinations of time-ramp duration and initial voltage were compared in this case, the accuracy achieved a rate of 99.89%. That is to say that only one of the samples was misclassified. Moreover, the misclassified prediction was among the healthy and first stage of failure (one broken bar). The training process reaches the referred accuracy after about 650 iterations, in epoch three, and it becomes stable at 99.89% after, more or less, 3000 iterations.
Once the capabilities of the proposed methodology have been exposed, in Table 9, it is compared with the results of other methodologies proposed for broken bar automatic detection in soft-started induction motors. Additionally, since there are not many works focused on soft starters, the results of other works focused on Direct Online starting are also included in the table. 2ff7e9595c
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