A premium automobile manufacturer in the UK spent over $3 billion on product recalls due to malfunctioning braking systems, rearview cameras, airbags, and other minor issues. Another European luxury automobile maker’s previous three annual reports indicated it spent about nearly $7 billion recalling products for problems ranging from door locks to batteries, fuse boxes, fuel tanks, and wheel speed sensors.
When automakers are focusing on developing self-driving and semi-autonomous vehicles, errors like these can hardly be overlooked. The entire network follows stringent quality control processes. Despite this, as the cases above demonstrate, lapses continue to occur. Anomaly detection can play a critical role across the industrial landscape in this context.
Understanding Anomaly Detection
Anomaly detection, described by Forbes as ‘one of the most underrated BI tools of 2020’, is an area of artificial intelligence that analyzes an organization’s data for deviations from normal behavior. Some inconsistent data points–known as outliers– may emerge, and detecting them will be critical to proactively averting scenarios like those described above. In some applications, the anomalies themselves are of interest, and the observations stemming from them could be the most significant in the entire dataset.
Anomaly detection has applications in cyber-security, fraud detection, fault detection, system health monitoring, detection of ecosystem disturbances with computer vision, medical diagnosis, law enforcement, manufacturing industries, and more.
Eliminating anomalous data can increase the accuracy of statistics such as the mean and standard deviation, improve data visualization, and enhance machine learning algorithms.
Typically, various tools and techniques are used for anomaly detection.
Anomaly Detection In Industry 4.0
Big data, machine learning, AI, and IoT devices are paving the foundation for a data-driven culture. IoT allows machines to exchange real-time data over the Internet. As part of the Internet of Things revolution, digital sensors and networking technologies are added to the analog devices we use every day, ushering in an era that we call Industry 4.0. According to some estimates, by 2025, there will be 64 billion IoT devices connected to the internet. This means that businesses will have to deal with a huge deluge of data.
According to the Boston Consulting Group, IIoT is one of the nine principal technologies that make up Industry 4.0. By combining these technologies, a “smart factory” will be created where machines, systems, and humans can work in harmony, coordinating and monitoring progress along the assembly line.
An important goal of Industry 4.0 will be predictive maintenance, which will be driven heavily by anomaly detection. As in traditional business applications and IT infrastructure, IoT devices can be monitored for errors. In industrial and manufacturing settings where IoT devices are used to facilitate modernization and automation, anomalies might indicate the need for maintenance on a machine. Identifying a potential problem early can help reduce unplanned downtime.
A McKinsey insight report found that advanced analytics can predict machine failure before it occurs, reducing downtime by 30% to 50%. Additionally, it increases equipment life by approximately 40%, improving productivity in all areas. An inadequate maintenance program can reduce an equipment’s productivity capacity by up to 20% while unplanned or last-minute machine downtime costs industrial manufacturing organizations $50 billion a year. By leveraging IIoT data, manufacturers can gain meaningful insights into their businesses and schedule pre-emptive equipment structural health checks.
Benefits Of Anomaly Detection In IIoT:
Cost and Time Savings: By detecting anomalies early on, you can ward off potential losses and liabilities. Often, noise and outliers can produce false positives, and get in the way of early anomaly identification. By using configurable time frames and historical pattern analysis, you can enhance detection latency and accuracy.
Deeper Insights: Detecting anomalies is only one step of a complex process that includes issue triage, root cause analysis, troubleshooting, and feedback-based system tuning. By engineering anomaly detection models from the ground up to provide advanced insights, you can investigate scores of issues like anomaly timeframes, severity scores, and correlated metrics. Advanced dashboards help visualize these insights.
Proactive Intervention: After the early detection of anomalies, you can generate insightful summaries to help with the investigation and fine-tune alert thresholds and severity levels according to operational feedback.
How Our IIoT Software Solution Helps With Anomaly Detection and Much More..
Valiance provides a proprietary IoT-based platform for remote monitoring and diagnosis of a power plant’s assets. Various sensors and scalable cloud software help operational teams look at asset health in real-time and give business stakeholders insight into plant efficiency and output.
For instance, one of our clients, who was struggling with manual intervention, wanted to enable centralized data collection through sensors and reduce the manual work involved. They also wanted to monitor their assets like turbines, and sensors such as thermometers and energy meters through certain dashboards.
Valiance delivered an IIoT-enabled platform to the client, enabling staff to view real-time data and monitor asset performance. Certain KPIs and threshold levels were defined at the backend, based on which the anomaly could be detected (notified to the client) and necessary steps taken in a timely manner.
In another instance, by enabling centralized data collection on near real-time basis, our platform allowed the customer to perform plant-wise and region-wise analysis on power generation targets, monitor the actual output, identify gaps, and coordinate timely intervention. Earlier this had not been possible due to delays in data collection, data consolidation, and sharing. However, after installing the platform, the time lag reduced from two to three weeks to less than a day, resulting in annual manpower savings of nearly four months.
Leverage Anomaly Detection To Improve Your Industrial Asset’s Performance
Anomaly detection is poised to become one of the most exciting developments in the business world, adding even more ‘intelligence’ to the AI revolution. But how can you get started?
Schedule a call today to find out.