As century passed and industry revolution become more and more advanced also human being?s life become more efficient and advanced. Also it is advanced time as Real Time system Wi-Fi or Cloud system, so it is need for our save our data. So, we are presenting Portable Condition Monitoring Unit (PCMU)
Portable Condition Monitoring Unit
As century passed and industry revolution become more and more advanced also human being’s life become more efficient and advanced. Also it is advanced time as Real Time system Wi-Fi or Cloud system, so it is need for our save our data. So, we are presenting Portable Condition Monitoring Unit (PCMU), which will monitor and predict health of industry 4.0. The authors are introducing eight (8) different parts of machines which effect health of plant or machine i.e. Temperature, Vibration, Acceleration, Humidity, Pressure, Magnetic Field, Noise and Gyroscope. By applying these methodologies and giving machine learning to any industrial machine or plant, it can increase maintenance and health as well as efficiency of industry.
Our project Portable Condition Monitoring Unit (PCMU) is used for both Condition monitoring as well as Predictive Analysis.
In Condition Monitoring, by monitoring the health of critical equipment we minimize our customer’s exposure to unplanned machinery downtime and thereby improve their profitability. It’s better than insurance.
While, in Predictive Maintenance, which is also very helpful for the machine’s life. When we know the future condition of any machine we will be able to overcome the problem of any machine.
As PCMU when connected to any industrial machine, it will update the condition as well as predictive analysis via Wi-Fi using simple Mobile Phone. As we are using multi-sensor device which has eight different parameters of machine which are as follows: acceleration, angular velocity and magnetic fields, as well as environmental conditions i.e. temperature, humidity, light, air pressure and noise.
Our Methodology is not bound by storage space or short range. What we basically do in our methodology is interconnection of different sensors (machine dedicated). Sensors that we are using are as follows: Temperature, Humidity, Pressure, Noise, Accelerometer, Digital Light sensor, Gyroscope and Magnetometer.
Our methodology have two steps, having Condition Monitoring as well as Predictive Analysis or maintenance.
In our 1st step, device that is sensor based, connected to the machine, sensors configuration is machine dedicated. Data is transferred via Wi-Fi module and real time condition of machine will show on mobile phone via android App as well Laptop.
While, in the 2nd step this transferred data is collected and send to cloud for the backup storage of data. We will train our machine according to collected data via artificial intelligence and then we’ll make different models which will do predictive analysis of machines accordingly.
One of the main benefit is, a place where normal human can feel insecure life because of heavy voltage or current or compact place so by making life secure, so by plugging PCMU on machine or plant, we can monitor and predict life of machines.
Also, we are in 21st century where almost everything is going automated, industry 4.0 is also coming on IoT based. In future it will be very helpful for industrial revolution. So, that we are providing an efficient portable device which will provide up to date and real time conditioning and predictive maintenance via Wi-Fi using mobile app or laptop.
In final deliverable, we are presenting a portable device which is called Portable Condition Monitoring Unit (PCMU) with multi-sensors having two different configurations, with Wi-Fi enabled via mobile app and laptop also used machine learning (Linear Recursion) for predictive analysis.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Arduino Uno | Equipment | 1 | 840 | 840 |
| Arduino Mega | Equipment | 1 | 1460 | 1460 |
| Surili Wi-fi | Equipment | 1 | 1800 | 1800 |
| DHT-11 | Equipment | 5 | 190 | 950 |
| ESP8266 | Equipment | 1 | 1050 | 1050 |
| GY-87 | Equipment | 2 | 1560 | 3120 |
| BMP 280 | Equipment | 2 | 480 | 960 |
| MPU 6050 | Equipment | 4 | 630 | 2520 |
| Light Sensor | Equipment | 2 | 1170 | 2340 |
| TMP-006 | Equipment | 2 | 360 | 720 |
| Noise Sensor | Equipment | 2 | 630 | 1260 |
| Solder | Equipment | 1 | 870 | 870 |
| Solder wire | Equipment | 1 | 220 | 220 |
| Solder Paste | Equipment | 1 | 130 | 130 |
| Solder Gun | Equipment | 1 | 3200 | 3200 |
| PCB Board | Equipment | 2 | 690 | 1380 |
| Acid (kg) | Equipment | 1 | 350 | 350 |
| Connectors (Male to Female and Male to Male) (in set) | Equipment | 4 | 400 | 1600 |
| Connecting Wires (in meter) | Equipment | 6 | 25 | 150 |
| Box-1 | Miscellaneous | 1 | 1800 | 1800 |
| Box-2 | Miscellaneous | 1 | 2200 | 2200 |
| Panaflex (5x3,3x2.5, Standee) | Miscellaneous | 1 | 1300 | 1300 |
| Brochure | Miscellaneous | 35 | 95 | 3325 |
| Thesis Binding | Miscellaneous | 1 | 950 | 950 |
| Petrol (litre) | Miscellaneous | 4 | 100 | 400 |
| TTL to USB | Equipment | 1 | 320 | 320 |
| Header double | Equipment | 4 | 45 | 180 |
| Total in (Rs) | 35395 |
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