What if you could stop a machine before a catastrophic failure? What if you could predict problems before they cause downtime? What if you could diagnose and resolve problems for your customers without ever leaving your office? The Service predictive analytics application from MachineMetrics, Inc. (Northampton, MA) answers these questions with AI-driven remote machine monitoring for OEMs and equipment distributors. Working from an industrial IoT platform, this application allows service teams to remotely monitor and manage machine assets in the field and at customer sites in real-time. Equipment suppliers can transform their approach to service with the ability to see, understand and take action on their customer’s real-time machine data from anywhere at any time.
“For years we have worked directly with manufacturers to improve their production equipment uptime, OEE and productivity with real-time machine monitoring and analytics,” said Bill Bither, the chief executive officer of MachineMetrics. “By allowing machines to be monitored remotely and in real-time, we enable OEMs and distributors to extend these benefits to their customer base while providing them with faster, better service. Our data science team works closely with the data to deliver optimized preventative and predictive maintenance specific to their machines to improve machine uptime.” When customers require after-sales service, they expect their problem to be solved with minimum delay. Because too many service calls require expensive and time-consuming on-site visits, equipment providers require remote access to monitor and assist with user operations to troubleshoot problems, resolve service events and monitor preventative maintenance tasks, all without leaving their office.
With the Service app, any machine maker or distributor can improve their customer service by resolving machine problems without the necessity of an on-site visit. Service managers and technicians can remotely monitor, manage, diagnose and resolve customer machine issues for any piece of connected equipment in the field and in real-time using a cellular Edge device from MachineMetrics. The historical and real-time machine data collected allows OEMs to gain insight into their customer’s equipment health and condition, identify new service opportunities with analytics and reporting, predict and deliver early warning of potential equipment failures, highlight elevated risk areas that lead to machine downtime, or even determine to take preventive action before it impacts machine performance. Any equipment supplier can install the Edge device on a new machine that is sold or they can retrofit any machine currently in the field. Edge has the ability to connect to the machine PLC and any additional sensors into the electrical cabinet of the machine and allows for the data to be shared with the equipment provider.
There is no need to install on a customer’s internal IT infrastructure because Edge comes with cellular support included. Once installed, the supplier can add the device to a list of machine assets accessible via the Service app and associate that machine with the location of the customer. Once the customer receives the machine and powers it on, that machine will appear active on the provider’s list of assets. Encrypted data is then streamed to a secure cloud, where the data is structured and aggregated to enable visualizations and analytics for service teams to monitor. Access to the historical and real-time data is available through open APIs. Real-time dashboards, historical analysis, and integrations with other systems can be built with these APIs. An analytics engine monitors any connected machine’s conditions and other manufacturing data points and initiates an action, such as text notifications when a monitor is triggered. These can include an alarm state, a limit being exceeded or any anomaly in machine health that is detected. A rules engine is provided for deploying monitors based on machine condition data, and advanced machine learning algorithms can be deployed for detecting anomalous behavior.
Using this data, equipment service providers can notify machine operators to change a tool before it breaks or even notify maintenance managers when an anomaly is detected that could lead to a breakdown. This ability can prevent thousands of dollars of equipment replacement costs and days of downtime, not to mention unearth new service opportunities that were never visible to service teams before. Data from thousands of connected machines allow the data science team to work with service technicians to identify trends and develop standard preventive maintenance and repair schedules that benefit both the service team and the customers. While service technicians focus on addressing issues that may immediately impact their customers, MachineMetrics keeps an eye on big-picture issues that benefit both the equipment provider and the end-user.
MachineMetrics, Inc., 5 Strong Avenue, Suite 201, Northampton, MA 01060, 413-341-5747, www.machinemetrics.com.
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