Every machine produces logs. Even those escalators and elevators at your favorite mall or office space. Here’s what will happen next. Those escalators will talk to a forecaster sitting at a remote location,and transmit data that will be tremendously valuable for their own maintenance.
But what messages are they transmitting? What’s the objective?
Typical messages that are getting collected include information on wear and tear of mechanical parts, peak load times, usages of interactive controls, activation of alarms and safety thresholds, and so on.
For Companies in the business of manufacturing these complex machines, safety has, and will always will be, be paramount . More importantly, servicing these machines with zero downtime is fast becoming a key business priority. Let’s see how log analytics of data generated by machines can act as service differentiators for these companies.
Forward-looking companies are introducing diagnostics as a service with proactive maintenance tasks being the key focus – the overall goal being Remote monitoring. Machine data from the logs is transmitted wirelessly to a platform ready to ingest the incoming data stream. Log processing includes understanding whether the data is time-based or non-time based. Once this is determined, the logs are processed using a parsing language to derive valuable operational intelligence.
What can this remote monitoring technology do?
This new service runs securely over the internet to ensure smooth running of thousands of escalators that are deployed at remote sites. This service ingests operational data including configuration change logs, micro-controller logs, error logs, and such. In turn, the logs are processed to report potential issues back to the operators even before they arise.
How does this service do that?
Logs from the escalators across the sites are automatically collected, classified, normalized, and aggregated into a log vault. A historical analysis is conducted with the new data set that comes in. Within minutes, powered by an ultimate machine log parsing language (SPL), any log file from the escalator is read and decoded to extract real-time intelligence. Dashboards collate the intelligence in the form of reports that help operators to take preventive actions. These actions range from predicting a potential fault to scheduling firmware upgrades.
Behind the scenes
The GLASSBEAM PLATFORM built on SCALAR indexes the logs as time- or non-time-based data. They are then stored in memory databases, making the platform truly capable of parallel processing hundreds of thousands of queries against Petabytes of machine data. Because these databases are essentially distributed collectors of objects, they can be cached in memory across cluster nodes making it easy to manipulate through various parallel operators. As a result, dynamic queries can be run and reports extracted in real-time.
Sitting on top of the analytics engine is the Rules and Alerts capability, where business rules can be defined. And alerts configured whenever an event appears to deviate from a standard behavioral pattern. Further, real time data streaming is possible because our stack is now INTEGRATED WITH APACHE SPARK.
It is scalable and repeatable at every site. The service is deployed over the cloud, making it possible for both IT and business stakeholders to harness real-time value from the same machine data source instantly at nearly zero overhead costs.