**3.3 Velocity sensor**

Velocity sensors are used to measure a frequency range of 1 – 1000 Hz. The sensors are suitable for vibration monitoring and balancing applications on rotating machinery. This type of sensor has a lower sensitivity for vibrations with high frequencies than accelerometers and is therefore less susceptible to overload. These sensors are used for high-temperature applications like above 700°F.

#### **3.4 Gyro sensor**

The main functions of the gyro sensor for various applications are angular velocity sensing, angle sensing, and control mechanisms. Gyroscope sensors are used in the car navigation systems, electronic stability control systems of vehicles, drones and radio-controlled helicopters and robotic systems. There are different types of gyros for different applications and they are: ring laser; fiber-optic; fluid gyro; and vibration gyros. Most used, vibration gyro sensors, use either piezoelectric or silicon transducer element in their construction. Gyro sensos, when used in conjunction with accelerometers, can keep track of the orientation of the system, thus providing a complete picture of the vibrating system.

#### **3.5 Non-contact sensors**

Most used non-contact vibration sensors types fall in the following categories: microphone or acoustic pressure sensor; laser displacement sensor; and eddy current or capacitive displacement sensor. Non-contact vibration sensors can be deployed on both new and old machines, even when they are hot, wet, in a hard-toreach places or too small for other sensor types.

#### **4. Predictive maintenance and AI**

Predictive maintenance as evolved over the years now is considered as an important IIoT application that can be implemented fairly-easily. It also connects with Industry 4.0 paradigm, big data and analytics, and machine learning. Over the years it has evolved from reactive, preventive, and reliability-based maintenance operations [17]. Many new online sensors are being introduced each year. IIoT enables machine condition control in the following manner: collection of real-time sensor data, perform data analytics, followed by corrective action either by an operator or autonomously by the machine. In this context data analytics is related to converting data into actionable information and can have implications for predicting future events such as predictive maintenance. Now with the revised promise of AI technology the predictive maintenance can evolve to prescriptive maintenance where a smart machine can help avoid the predicted failure. In essence the following steps are required to implement such program: identifying critical assets, creating a database of relevant information, understanding failure modes, developing models for predicting failure modes, test the predictive model (s), and deploy for real-time operations as outlined above.

Even though there are many new sensors available for monitoring and control, there are many areas where manual steps are still required for maintenance activities due to lack of appropriate effective sensors. Thus the means of data acquisition can be multimodal. It can be obtained from samples collected manually and analyzed in the laboratory, monitored in real time with online sensors, acquired using portable data collectors, or examined by operators and engineers. In addition other tests and inspections at the machine site can help complete the picture and establish greater confidence in what's happening now (or not happening).The IIoT and AI technologies cannot make all other forms of condition monitoring obsolete, but they are powerful enablers. Consider an example in a machine shop with CNC routers, lathes, EDM machine, and metal 3D printers etc. Over a period of their operation the following can change: machine age which can lead to changing vibration, heat, acoustic emissions, displacement, alignment, balance etc.;oil and filter age; temperature and humidity; load conditions; looseness of parts; and operator handling. These changes may require further monitoring and adjusting such things as: oil flow rate and temperature control; grease dosage rate and frequency; viscosity correction; additive replenishment; machine operation; maintenance and inspection requisitions. Because of this complexity it is possible to design and develop different software applications that can address the above challenges by integrating more soft and hard automation processes.

AI Machine Learning involves the following steps: identifying the data set and corresponding sensors; collecting the data; preparing the data set for training, validation and testing; choosing a model and algorithm; perform model training calculations; evaluate the model; tune the model; and deploy the model for prediction.

## **4.1 CNC machine condition monitoring system**

As mentioned above a major objective for manufacturing industries is to reduce the cost and improve safety and production. During 1980's to 1990's, the cutting tool was replaced based on wear of the cutting tool. But since then, tool condition monitoring systems have evolved and are used quite extensively to achieve the following objectives: early detection of cutting tool wear; maintaining a machining accuracy by providing a corrective action for tool wear; and prevention of cutting tool from breakage [18]. Using modern high precision sensors for sound, temperature and humidity, *vibration, strain/force, power,* and other appropriate *analog or digital sensors,* the user can monitor machining conditions using different software analysis options. Most of these common sensors provide a 0 to 10 VDC analog signal, and 4 – 20 mA current signals. The data collected from these sensors can lead to limit analysis, spindle bearing faults, and frequency analysis etc.

In a recent study the authors used a MEMS installed sensor on a CNC machine with Fanuc controller to measure vibrations for maintenance purposes [19]. First, they carried out three case studies: optimal cutting values with a worn-out tool, cutting values with a new tool that breaks and does not break. After the analysis of case studies, a maintenance method was chosen according to TPM and TQM program guidelines. The analysis of the result lead to a proposed maintenance program. But the authors did not use any of the AI tools available to develop a predictive maintenance program.

#### **4.2 Vibration detection application use case**

In this case study a lathe is used to cut a stock bar into different size pieces. This lathe has an auto-fed bar feeder delivering 12-foot bar stock to the unattended running machine [20]. A significant number of parts were being scrapped due to irregularities in the bar, causing dimensional and finish errors. These irregularities in the bar could lead to a lot of scrap metal and damage the spindle. A commercial solution was used to install vibration sensor and connected it to the CNC control to monitor certain characteristics of the lathe. When excessive bar feeder vibration levels were detected the software would automatically signal the CNC to reduce spindle RPM until the vibration levels are acceptable to make good parts. If RPM must be reduced too much, and parts cannot be cut, an alarm was generated to inform the operator to stop the machine to remove the bar. In this case this process can be further automated, but it would require local technical resources to implement them.

#### **4.3 Wireless monitoring**

To monitor vibration measurement remotely the following protocol options are available: WirelessHART; ISA100; WiFi; Bluetooth; LoRa; and Proprietary. The following factors needs to be considered in selecting the appropriate protocol: built-in security; reliability; bandwidth; power consumption; supported configurations; interoperability between vendor products; and network maintenance. Another critical factor that needs to be considered is where do you perform the calculations related to the data analytics option for the wireless monitoring system and what information is going to be transmitted wirelessly. That is where and how would you view and store the raw sensor data, see the real-time trend and historical data, and send the alarms to mobile devices and remote computers. How and where would alarm be calculated.

WirelessHART (IEC 62591) is a field proven technology with very wide installed base as it was built on HART protocol. It has been an international standard (IEC

#### *IoT Applications Computing*

62591-1) since March 2010. This protocol offers the following advantages: it has small defined packet structure for reduced bandwidth and interoperability; built in security; uses mesh networking to ensure reliability; and can be expanded easily. A wide variety of device types are available from many automation equipment suppliers. SEPT Learning Factory provides the following two options for student projects: vibration transmitter (Emerson) as depicted in **Figure 5**; and a portable machine health analyzer (Emerson). The transmitter monitors/transmits WirlessHART vibration and temperature in hard-to-reach locations. It provides complete vibration information including overall levels, energy bands, high resolution spectra, and wave forms. It provides information for bearing and gear diagnostics. The PC hosts the specialized software provided by Emerson. The WirelessHART access point easily integrates into any host via Modbus TCP with capabilities for detailed diagnostics via a commercial software suite or custom-built Software. The wired option with dashboard shown in **Figure 6**, and user interface software components, **Figure 7**, required to build the dashboard for the Haas CNC vibration and other parameters monitoring has been implemented as well.

**Figure 5.** *Wireless HART sample set up.*

On the other hand, the portable machinery health analyzer (Emerson) is used by the students working in the Learning Factory for vibration data collection and field analysis. This system can provide route vibration collection; advanced vibration analysis; cross-channel analysis; transient analysis; dynamic balancing; motor monitoring; and ODS modal analysis. It wirelessly uploads route data and corrective maintenance jobs from the field to AMS Machinery Health Manager (Emerson) for analysis and reporting. AMS Machinery Manager integrates data from multiple technologies, including vibration, oil analysis, thermography, and balancing into a

*IIoT Machine Health Monitoring Models for Education and Training DOI: http://dx.doi.org/10.5772/intechopen.99032*


**Figure 6.** *HAAS CNC web user Interface.*

single database to deliver the predictive intelligence necessary for increasing availability and reliability in the plant.

Another industrial IoT wireless machine health monitoring system with the following sensors: vibration sensor, thermocouple, AC split core current sensor, and ambient air temperature sensor is also available to the students for experimentation [21]. This low-cost device samples vibration, RMS current and temperature data and sends after a user-defined time interval over the wireless network. The vibration sensor samples 3-axis vibration data for 500 ms and then calculates RMS, Maximum, and Minimum vibration readings then combines these data with temperature values in a data packet and transmits the result to modems and gateways within wireless range. After each transmission it goes back to sleep, thus minimizing power consumption. This system uses DigiMesh® protocol, from Digi.com for wireless transmission of data, which automatically hops data from gateway to gateway until it arrives at the desired destination. The data on the other end is either received by an IoT gateway or an IoT modem, connected to local Learning Factory MQTT broker, and ultimately displayed on the dashboard designed by the students either using Grafana or Node-Red.

**Figure 7.** *User Interface software components.*

It is also important to take note that the overall vibration data is not always a good indicator of machine health due to the following aspects of vibration measurement: fluctuates heavily due to process changes and insensitive to other failure modes such as bearing faults, gear defects, lubrication, and pump cavitation. Since the vibration analysis is based on raw vibration data other values such as RMS, peak value and impacting g's can be obtained as well. There are many approaches in interpreting the information from the raw vibration data. For example, contrary to claims in literature, it has been shown that RMS and peak values are good indicators of the gearbox health if used properly [22]. Another example is the analysis of vibration data from a process pump which showed that the overall vibration indicated good health of the asset while hidden in the raw data was the rising g values that indicated a bearing defect. The impacting analysis g values from the raw data can provide useful information for the following types of faults: bearing faults, gear defects, lubrication, and pump cavitation [23].
