The Internet of Things holds tremendous promise to shift failure detection and parts replacement from reactive systems, responding to breakdowns, into predictive maintenance systems that proactively report impending failures. Systems that exploit this move to IoT-based solutions will be able to schedule replacements before components fail and thereby minimize downtime. The IoT provides the potential to make operational systems more efficient and intelligent, based on understanding of the incoming sensor data. To achieve this worthy goal, data scientists will employ Big Data strategies that analyze sensor outputs and report anomalies.
Capturing Anomalies In Big Data Nets
Big Data tools are ideally suited to transform inputs from IoT sensors, determine the smallest detectable variations, and recognize the changes that represent the likelihood of failure. Developers can tailor solutions to the particular system in each IoT platform because it is the unique characteristics of each set of sensors that provides the opportunity for measurable insights.
The real power of IoT sensors is not in the individual units, which may work at relatively slow data rates; it comes from collecting the outputs of many sensors. It is the flow of data that generates the raw materials from which Big Data platforms and tools extract the useful information. Passing date through real-time analytics has the predictive power that comes from recognizing patterns within the data stream.
The first stage of anomaly detection is to select which variables are diagnostic and to transform the data using Big Data tools. This strategy finds the characteristics of abnormal behavior within the information, which appear as changes in the distribution of the key data elements over time. In operation, the procedure uses real-time data streams to compare to the patterns of normal operation and differentiating the expected modes of failure.
Learning To See Patterns In Data Streams
Anomaly detection does not require the breakdown of a component or system to trigger alerts. The capabilities of Big Data pattern recognition systems promise to deliver predictive maintenance, which will enable managers to withdraw assets from the work cycle or to schedule repairs during normal downtime. Such capabilities will reduce the time lost during normal operations when interruptions threaten service continuity.
Additionally, Big Data platforms that apply predefined transformations to IoT sensor data can help to reduce the time it takes to develop the benchmark standards against which to check normal operation. Data scientists can review graphically displayed data to determine the best combinations of sensor variables to monitor for predictive maintenance, which will reduce setup times.
Anomalies Offer Predictive Opportunities
Detecting anomalies in IoT applications provides a valuable tool in Predictive Maintenance, to eliminate unscheduled downtime, which saves cost and improves service delivery. Big Data solutions will reveal changing patterns that can alert operators to the potential for trouble.
Predictive maintenance can provide greater efficiencies in industries as diverse as manufacturing and healthcare. The combination of the IoT and Big Data in one strategy promises to transform anomalies into opportunities that deliver measurably improved returns on investment to the organizations that apply them.