Senseye
Redefining Predictive Maintenance Using AI

It is obvious that all machines are subjected to wear and tear in the long run. Nevertheless, the inability of manufacturers to appropriately monitor the wear and tear in a machine will eventually lead to machine breakdown and unplanned downtime. This brings about significant revenue loss and operating cost to a manufacturer. According to the joint study by the Wall Street Journal and Emerson, downtime costs industrial manufacturers an estimated 50 billion dollars per year. The study also reveals that 42 percent of this unplanned downtime is due to equipment failure. In the wake of such adverse happenings, industry veterans are seeking predictive maintenance to combat machine failure threats. However, most of the predictive maintenance solutions available in the market follow a traditional approach that involves periodic manual analysis and expert calculations—leading to a laborious and time-consuming process. In order to mitigate these impediments Senseye, a leading software provider, uses artificial intelligence (AI) to automate condition monitoring and prognostics analysis to predict equipment failure and machine breakdowns. This software is deployed in shop floors to help in the production line workers to constantly monitor the health of the factory machines.

Talking about using AI in predictive maintenance, Dr. Simon Kampa, co-founder and CEO of Senseye says, “Relying on AI to undertake monitoring and analysis of thousands of machines only requires a slight shift in mindset and focus.”

Senseye’s proprietary cognitive algorithms allow its software to be used on any machine, by any manufacturer. Moreover, there is no need for installing additional hardware like IoT sensors in the machine to monitor the machine health. The AI-powered software will monitor the machine health using machine condition and operations data from the factory data history, and database solutions. Alongside, Senseye extensively secures its client’s data using modern cryptography methods such as encrypted data transmission (TLS 1.2-AES 256).

Using Senseye to analyze machines automatically, OEMs are able to predict when and what maintenance is needed, scheduling remedial work before problems impact the end user, ensuring a high standard of quality, throughput, and uptime. Senseye also helps manufacturers predict the remaining useful life (RUL) of machines and equipment that are installed in the manufacturer’s customer site. As a result, it allows manufacturers to focus on delivering exceptional products and strengthening customer relationships. Today, the company partners with other solution stacks, to offer versatile products for its customers. For instance, Senseye partners with IoT middleware, factory data history, and other products which bolster the output of the solution. With such partnerships, the software firm has kindled several success stories and testimonials to review.

One of the global leaders of the automotive industry, Nissan leverages Senseye’s intuitive software to handle predictive maintenance meticulously. Over 2,500 assets including robots, conveyors, drop lifters, pumps, motors and press/stamping machines, are remotely monitored using this software. At Nissan, more than 200 maintenance users actively use Senseye to optimize maintenance activities and make repairs months before predicted machine failure. After witnessing the tremendous improvement in machine uptime, Damian Wheeler, Nissan UK’s Engineering Director mentions, “Senseye is supporting our Predictive Maintenance Program across three production facilities and has helped us lower overall downtime and increase Overall Equipment Effectiveness (OEE). With such proven results that include a 50 percent reduction in unplanned machine downtime and a 40 percent reduction in maintenance cost, it’s no doubt, Senseye is one of the frontrunners among predictive maintenance solution providers.