Identify Known Anomalies

Identifying anomalies is a key element of any analysis effort. Identify or remove known anomalies at the point of data capture to save time and money.

examples:

• During machine startup and stops, sensors may produce values well outside the typical range seen during machine operation.
• A newly replaced sensor may produce a shift in the measurement baseline, mark sensor changes in the data set and consider calibrating new sensors.
• Mark the data set when the machine is shut down improperly for example, unexpected loss of power.

Normalize Data

Develop a standard for how data should be captured by to ensure that across all the systems the recorded data is in one standard format, this can save valuable time.

examples:

• Is a temperature reported in Fahrenheit or Celsius?
• Is it reported as an integer or a floating point? How many decimal places are required?
• If the value can be negative, what format is used to record the negative number?
• What is the maximum and minimum value for the given sensor?
• What values indicate failure for a given sensor?

Time

Time is the most critical parameter in time series data. Develop a time stamp strategy that can be deployed across all systems including time calibration and time adjustments.

Examples of the importance of Time:

• The time format including date and year
• Time resolution
• How is the time set and calibrated
• How is the time verified and how often is it verified
• How are errors reported

Sampling Data Too Infrequently

Assume a company is collecting data in the field. The system is recording pallets stacked monthly. In the graph to the right, the number of units produced drops by 50% prior to a customer returning a machine. This may be an indication that a user is going to terminate their lease and return the machine.

Solution

If data was collected per day or hour it would show that customers use the product at the same level prior to returning the system. The falloff is because leases terminate in the middle of the month. Additional context (Lease return date) or increasing sampling frequency (Daily) would eliminate this incorrect conclusion.