INTERNET OF THINGS
Predictive Maintenance System
Introduction
With increasing automation in production and packing, the number of equipment used are increasing day by day. Maintaining all these devices, on a regular basis, has become a tire-some task to perform.
To overcome the problem of tiring predictive maintenance process, we have designed an IoT based system to monitor all the motors, pumps, compressors, and related equipment by sensing their temperature as well as vibration in all 3 axis.
The continuous monitoring of these devices provides with many benefits for the user.
TIME SAVING: No more spending time in checking each and every motor during preventive maintenance schedule. Since the data of all motors is available at one place, so it will save lots of time involved in manual checking.
LESS MANUAL WORK: Rather than going to each and every motor, the user can simply scroll through line-wise screens in software and check condition of each and every motor fitted with sensor.
AUTOMATED ALERTS: In case any motor shows abnormality, it will be displayed in the software.
MUTIPLE FAULT DETECTION: Rise in temperature or vibration will occur in case motor needs more oiling, change of bearing, or tightening of screws for mounting. Even if there is any issue with coupling, it will lead to increase in vibrations, thereby alerting the user.
Applications
Rotating Machines
Underwater Submersible Pumps
CNC
Heat Exchangers
Rolling Mill
Pumps
Gearbox
Reciprocating Machines
Conveyor Systems
VNC
Boilers
Spindle Motor
Motors
ID & FD FANS
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Goal of CBM
The main goal of condition based maintenance is to help you optimize your maintenance resources by performing maintenance work only when needed. CBM makes use of the Predictive maintenance tools to detect, monitor, analyze and detect anomalies in machines.
Measurement Methods
There are two types of vibration measurement methods “Permanent online vibration monitoring system” and “Portable off-line monitoring system”. Either one is selected generally depending on importance rank of equipment.
1. Online Monitoring
Applicable for Scenario where:
2. Offline Monitoring
Applicable for Scenario where:
Machine Monitoring Functions and Features:
WIFI SPECIFICATIONS:
| protocol | 802.11 b/g/n support |
| Frequency Range | 2.4GHz to 2.5GHz (2412 MHz to 2484 MHz) |
| Transmit power | 802.11 b: +19.5 dBm, 802.11 g: +16 dBm, 802.11 n: +14 dBm |
| Receive Sensitivity | 802.11 b: -91 dbm (11 Mbps), 802.11 g: -74 dbm (54 Mbps), 802.11 n: -71 dbm (MCS7) |
| Antenna | Integrated in device |
| Security | WPA/WPA2 |
| Encryption | WEP/TKIP/AES |
| Network Protocol | HTTP, MQTT, TCP/IPv4 |
SENSING & PROCESSING:
| Vibration Sensor | MEMS based Tri-axial accelerometer |
| Frequency range | 10 Hz to 1300 Hz (X, Y and Z axis) |
| Default configuration | F-max: 1300 Hz and LOR: 200 |
| Output data rate (Sampling rate) | Up to 3.3kHz |
| Sensitivity | 0.122 mg/LSB (±4G) |
| Resolution | 16 bit |
| Derived values | • 3-axis acceleration RMS and velocity RMS • 3-axis acceleration and velocity FFT • 3-axis spectral features as per configuration |
| FFT frequency resolution (delta-f) | Configurable between 0.2033 and 6.5039 Hz |
| Shock Tolerance Range | 10,000g for 0.2ms |
| Temperature sensor Range | -20 to 85°C |
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