1.Remote assets tracking

Remote asset tracking involves using technology to monitor and manage assets, such as vehicles, equipment, or inventory, from a distant location. This is typically achieved through the use of GPS, RFID, or other tracking technologies.

Onsite maintenance costs you time, money and resources, so you’re keen to avoid being on-prem whenever possible. The key to staying offsite is to monitor your assets remotely. You can do this with Industrial IoT remote asset monitoring solutions that supplant your legacy maintenance schedules of physically-present inspections, resulting in a much lower OpEx for tracking, preventing and preempting connectivity problems. 

Data-Informed Insights

Failure of critical assets means frustrated customers, delayed operations and financial loss. In order to preempt and/or deal with downtimes, asset monitoring is key. But beyond that, the value of remote monitoring lies in the fact that the data gleaned from remote device monitoring can be leveraged for KPI reporting capabilities, enabling informed business decisions by executives based on smart asset tracking and smart metering. 

Therefore, industries are replacing their former maintenance schedules and visual inspections with new, wireless, cloud-based technologies for remote end-to-end site monitoring, including predictive/preventative maintenance.

Here’s a sample topology design for an IIoT end-to-end solution for wireless asset monitoring centered around the RAD SecFlow Gateway: 

Due to the challenges for remote asset monitoring, organizations are looking to IIoT with edge computing as the solution. The ideal RAMS allows for ultimate flexibility of integration – one that moves away from a cumbersome, multi-domain, disaggregated fragmented monitoring to unified, aggregated end-to-end wireless monitoring, over cellular or fiber networks, public or private, be they 4G, 5G, PLTE, 450MHz (450 LTE) CBRS, LPWA (low power wide area) or other network options for intelligent monitoring.

Edge Computing

Edge Computing allows for local data processing by sensor-rich assets, rather than via a centralized control center. This saves equipment costs and space and enables future functionality flexibility. Other Edge Computing benefits include ultra-low latency, quick action on detected anomalies, lower mobile data costs, predictive maintenance based on smart data collection, and reduced cyber security needs due to a lower number of components.

2.Assets health and condition monitoring

A​​​​​​n open/integrated system utilizing components with short lifecycles requires reliable security measures. In order to maintain stable and continuous operation, a proper understanding of the system’s overall health is essential. An IoT-based asset management system for health and condition monitoring in healthcare facilities can revolutionize the way medical equipment and devices are tracked and maintained.It provides a real-time view of asset health and condition status, delivers early warning of potential failures, and highlights elevated risk areas.

In healthcare facilities, medical equipment plays a vital role in patient care. From MRI machines to infusion pumps, ensuring the reliability and availability of medical devices is paramount. IoT-based asset monitoring systems can track the condition of medical equipment, alerting staff to potential issues before they escalate and ensuring that life-saving devices are always ready for use.

Features

• Monitor: Reduce unplanned downtime and increase availability and reliability by helping to ensure critical assets and systems are monitored and protected from emerging threats.
• Optimize productivity: Improve workforce productivity by prioritizing maintenance based on criticality and cost (condition-based), rather than schedule-based maintenance practices.
• Manage risk :Protect the health and safety of employees, the environment, and business objectives, by reducing asset-related incidents and unplanned downtime.
• Predictive maintenance: Reduce costly emergency repairs by detecting problems early, turning unplanned downtime into planned/scheduled downtime.
• Lower total cost of ownership (TCO): Reduce TCO delivered via a Software-as-a-Service (SaaS) model, providing flexibility and access to business insights where it matters—with edge, cloud, and hybrid configurations.
• Faster time-to-value: A standardized way to connect machines, data, and people with a consistent interface for superior user experience, dynamic scalability, and extendibility to grow functionality as business needs evolve.

Techniques used:

• Vibration analysis: Measures vibration levels in equipment to detect anomalies that might indicate bearing wear, misalignment, or other issues.
• Thermography: Uses thermal imaging cameras to detect temperature variations that can indicate overheating, impending component failure, or insulation problems.
• Oil analysis: Analyzes lubricant samples for signs of wear debris, contamination, or changes in viscosity, indicating potential problems within the machinery.
• Ultrasonic testing: Uses high-frequency sound waves to detect cracks, voids, or other defects in materials.
• Acoustic emission (AE) monitoring: Detects and analyzes sound waves emitted by machinery due to stress, cracks, or leaks, helping identify potential failures.
• Data analytics: Advanced analytics tools are used to analyze sensor data from various sources, identify trends, and predict potential issues.

Asset lifecycle management encompasses the entire lifespan of an asset, from acquisition to disposal. It involves various stages, including procurement, maintenance, utilization, and retirement. Effectively managing assets throughout their lifecycle is crucial for maximizing their value, minimizing downtime, and ensuring compliance with regulations and standards.

Leveraging IoT for Asset Lifestyle Management

IoT technology offers a plethora of capabilities that can transform asset lifecycle management:

1. Real-time Monitoring:

IoT sensors embedded in assets can continuously monitor key parameters such as usage, performance, and environmental conditions. This real-time data enables proactive maintenance, allowing organizations to address issues before they escalate and optimize asset utilization.

2. Predictive Maintenance:

By analyzing data collected from IoT sensors, organizations can predict when equipment is likely to fail and schedule maintenance proactively. Predictive maintenance reduces downtime, extends asset lifespan, and lowers maintenance costs.

3. Asset Tracking and Optimization:

IoT-enabled asset tracking systems provide real-time visibility into the location and status of assets. This enables organizations to optimize asset allocation, improve workflow efficiency, and prevent loss or theft.

4. Remote Diagnostics and Troubleshooting:

IoT technology allows for remote diagnostics and troubleshooting of assets, reducing the need for onsite inspections and service calls. Technicians can remotely access asset data, diagnose problems, and even perform repairs or adjustments remotely, saving time and resources.

5. Performance Analytics:

IoT platforms can analyze historical asset data to identify trends, patterns, and anomalies. This data-driven insight helps organizations make informed decisions about asset maintenance, upgrades, and replacements, optimizing asset lifecycle management strategies.IoT allows asset managers to track their portfolio assets throughout their lifecycle, maximizing profitability.

IoT-driven solutions automate repetitive tasks and speed up workflows. This reduces errors and increases team productivity.

From identifying the requirement and continuing to its execution steps, IoT asset management can process it all. It starts with dashboard management where every detail related to the asset is viewed. Automated notifications are sent to support centers to assist in case of an emergency. If there’s some problem with the asset’s condition, it automatically triggers an alarm that informs the plant manager about the issue in the functioning along with the root cause, thereby saving the plant manager’s time in finding the problem.

IoT based asset lifecycle management is a digital version of a human asset manager. It manages the dashboard, notifies the engineer about the system malfunction, reaches out to the support center for help, checks inventory and orders the spares that are out of stock, sets a reminder for maintenance, and all the other operations required to manage the asset; without any break or error.

Workflow automation

Using workflow automation in IoT asset management can help businesses to complete the work and trigger events to proceed to the next step. For example, whenever equipment is out of stock, a human worker, say, the manager would be involved to view the details of the inventory and then send a purchase request. Or you can automate the procurement and dispatch process so that managers don’t need to intervene and the workflow can be completed automatically with fewer errors and delays. The application of workflow automation can be tailored for each industry. Heavy machinery can use workflow automation to carry out their functioning, HR workflow automation to accept the applicant, send emails, etc.

These are just a few of the examples of use cases for IoT asset management solutions. Due to size and price, the applications of IoT for asset management are limited to tracking and automation for heavy machinery. However, the industries have a huge demand and with automation and digital revolutions in the coming years, the asset management for IoT will have a wide spectrum of applications used by large and small scale industries.

Implementing IoT-based Asset Lifestyle Management

To harness the full potential of IoT for asset lifecycle management, organizations should consider the following steps:

1. Assess Needs and Goals: Identify the specific assets to be monitored and define the goals and objectives of the IoT implementation, such as improving asset reliability, reducing maintenance costs, or enhancing operational efficiency.
2. Select Appropriate Sensors and Devices: Choose IoT sensors and devices that are suitable for the assets being monitored and the environment in which they operate. Consider factors such as durability, connectivity options, and power requirements.
3. Integrate with Existing Systems: Ensure seamless integration of IoT solutions with existing asset management systems, enterprise resource planning (ERP) software, and other relevant platforms to facilitate data exchange and workflow automation.
4. Establish Data Security Measures: Implement robust cybersecurity measures to protect sensitive asset data from unauthorized access, breaches, and cyber threats. This includes encryption, access controls, and regular security audits.
5. Train Personnel: Provide training to employees on how to use and interpret data from IoT-enabled asset monitoring systems. Encourage collaboration between maintenance teams, IT departments, and other stakeholders to maximize the benefits of IoT technology.

From reducing equipment maintenance costs and enhancing asset utilization to extending machine life and optimizing field crew efficiency — IoT-based predictive maintenance offers a range of benefits. Given the digital day and age we live in, it is hard to deny that the Internet of Things (IoT) has no role to play in the manufacturing domain. In this article, we will look at how IoT-based predictive maintenance is making waves for manufacturers.

The technology has already been in use to a great extent in many manufacturing initiatives for developing smarter factories of the future. Sweden-based engineering company Sandvik, for instance, teamed up with Microsoft for creating sensorized cutting equipment.

The technology combines streaming analytics, data collection, and machine learning algorithms to notify engineers when the tool bit maintenance is required or to offer an alert of impending failure. On the other hand, ABB, an engineering multinational specializing in robotics, has developed a predictive maintenance system for driving applications in manufacturing.

In this scenario, cloud computing, machine learning algorithms, and sensors combine to offer an overview of equipment performance for keeping the production running as planned. This has been beneficial for the Italian steel pipe manufacturer, Tenaris.

It uses predictive maintenance to track high and low-voltage motors operating critical fans and pumps 24/7. The system has been used for collecting and analyzing vibrations to highlight failure and any anomalies in power or voltage that indicate a short circuit.

1. Identify assets that need predictive maintenance

Before you start deploying the technology in your manufacturing unit, you must know that not all machines will need predictive maintenance. To identify which ones would need it, think about the impact of minimal downtime of specific equipment on your bottom line.

Sort the identified assets based on their past downtime incidents and the business loss they can lead to. That way, you will know which ones to pick up on priority for predictive maintenance.

2. Choose the right software

With hundreds of systems available in the market, searching for the right predictive maintenance platform can be a bit challenging. You have to find the perfect fit because it is a costly investment, unlike purchasing clothes off the rack!

Speak to various vendors to get clarity about how their products can help. Schedule a demo or two. Ask if you can try a few features of their IoT-based predictive maintenance system for a defined timeline. While doing your research, check out user reviews about the IoT development company to get an idea of how their experience was.