IoT Solutions-enabled Predictive Maintenance Leveraged By Manufacturers

The manufacturing sector faces a myriad of productivity challenges including expensive replacement costs of assets, monitoring the use and location of high-value assets, equipment downtime, and human errors. The goals of maintenance professionals in a manufacturing business are always the same: optimize machine downtime and maximize asset performance by predicting its maintenance and failure. Internet of Things (IoT)-based Predictive Maintenance (PdM) system collects real-time information about the performance and operating conditions of assets that allows manufacturers to minimize manual efforts required for maintenance and increase asset lifecycle with reduced operational costs.

Predictive Maintenance application is based on condition-based monitoring technologies, statistical process control and historical performance data for early detection and elimination of equipment issues. It helps in avoiding unplanned downtime or unnecessary expenditures. With the advent of Industry 4.0 known as Industrial IoT (IIoT), manufacturers are able to leverage new technologies in order to monitor and gain deeper insights into the operating condition of their assets in real-time, turning a typical manufacturing facility into an advanced factory.

How Predictive Maintenance (PdM) Works

Predictive Maintenance technology has been evolved with enhanced capabilities over the years. Today, manufacturers do not need to import data to spreadsheets and get insights manually. With the increasing usage of IoT-enabled PdM, businesses can accurately predict maintenance tasks. However, some factories are still lagging behind in the analytics capabilities. According to our research, the average manufacturing company discards 98% of all data it collects due to not having the operational analytics capabilities to integrate that data into its operations.

IoT service providers such as Eastern Software Solutions stepped in to fill the gap between data and insights for industrial businesses with IoT-enabled Predictive maintenance application. It utilizes data from various modern and legacy sources, such as critical equipment sensors, Enterprise Resource Planning (ERP) systems, Computerized Maintenance Management Systems (CMMS), machine’s Programmable Logic Controller (PLC), and production data. IoT-based devices couple this data with advanced prediction models and analytical tools to forecast maintenance and failures accurately, and address them proactively. Today, it is one of the most widely used applications in the manufacturing facilities to improve the maintenance and lifecycle of the assets.

IoT-enabled Predictive maintenance includes 3 main components:

1.Gathering Sensor Data

IoT-enabled predictive maintenance system utilizes sensors and non-destructive techniques to evaluate an asset’s performance and operating conditions. These sensors can perform spot checks at regular intervals while they are in normal operation. Some of those sensors measure the following parameters:

• Infrared thermography: detects temperature using infrared imaging
• Electrical current analysis:measures voltage and current
• Acoustic monitoring:detects gas, liquid, or vacuum leaks on a sonic or ultrasonic level in an equipment
• Vibration analysis:monitors velocity, displacement, or acceleration to identify vibration patterns
• Oil analysis:checks lubrication of machinery and assesses oil condition

Thus, Predictive Maintenance allows real-time monitoring of these variables for immediate interventions and solves issues before they occur.

2.Transmitting Data

Once sensors have gathered asset conditions and performance data through IoT-enabled predictive maintenance system, it is stored and organized at IoT platform. It enabled multiple systems to extract and exchange real-time data from different sources. Business can further integrate IoT platform with Business Intelligence tool to analyze gathered data for crucial business KPIs and gain business insights in addition to predict asset maintenance and failure.

3.Forecasting

Based on the analysis of collected data, IoT-enabled predictive maintenance has the capability to identify patterns and forecast when an asset is expected to fail or require maintenance. The algorithms use predetermined rules to compare an asset’s present performance against its benchmarked standards, determine the level of deviation, and estimate the time of maintenance.

Benefits of IoT-based Predictive Maintenance Use Case

Implementing Industrial IoT-enabled predictive maintenance has saved successful manufacturing businesses a significant amount of money and time by decreasing machine downtime. Any organization that relies on expensive machinery like manufacturers, automotive, or construction can achieve the following benefits from the implementation of IoT-predictive maintenance:

• Improves ease of maintenance scheduling
• Reduces Maintenance, Repair, and Operations (MRO) inventory costs
• Ensures minimal disruption to productivity
• Increases asset uptime
• Gives real-time monitoring of current asset condition
• Increases assets lifecycle

Industrial application of predictive maintenance

With the decreasing cost of technology and barriers to implementation, an exponential increase in the global adoption of IIoT use cases can be seen in manufacturing and many other industries such as transportation, banking, construction, oil & gas, healthcare, agriculture and many more. Leading enterprises in these industries are succeeded by understanding how to eliminate operational bottlenecks, reduce manual intervention and efforts, and boost assets efficiency. Moreover, IoT-based Predictive analytics could increase the overall competitiveness of individual companies or entire industry.