Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the outcomes of AI models understandable to humans. XAI is essential for ensuring transparency, accountability, and trust in AI systems, particularly when they are used in critical decision-making processes. Instead of treating AI models as “black boxes,” XAI provides more context to clarify why a model made a certain decision, what factors were considered, and how different inputs influenced the output. ULINK DA Drive Analyzer’s Symptom Radar Chart is a good illustration of XAI for drive health prediction.
Use of XAI in ULINK DA Drive Analyzer
ULINK DA Drive Analyzer continuously monitors drive health data and then analyzes drive failure possibility. Its dashboard displays a summary of how many drives are healthy, how many are at moderate risk, how many are critical risk, and how many have health indicators that have crossed system-specific thresholds.
To explain or understand why a drive has been categorized as healthy or at risk, you can use the Symptom Radar Chart, a visual tool that presents a comprehensive overview of a drive’s health by displaying various symptoms or metrics that could indicate potential issues.
Visual Explanation of Drive Health
The Symptom Radar Chart is a polygon-shaped radar chart that visually represents the relationship between different drive health metrics along five symptom axes: S.M.A.R.T., Temperature, Drive-Detected Issues, Self-Test, and Host-Detected Issues.
Identification of Key Risk Factors
Whenever a particular axis (group of symptoms) is showing active symptoms, that axis will show a score below 10 (which is the optimum score). A lower axis score indicates poorer health along the corresponding health axis. For example, if a drive has a lower axis score of 7 on the S.M.A.R.T. axis, and 7 is the lowest score of any axis, then the drive has a 70% chance of remaining with the user over the next year according to historical data.
If the user clicks on the axis, it will highlight the individual corresponding symptoms, and brief definitions of each symptom can be seen by hovering over each symptom. More detailed definitions can be found in DA Portal by clicking on Help >> Help Search, and typing the name of each symptom. This type of exploration can provide insight into how each active symptom might be affecting the drive’s health and longevity.
Actionable Insights
Once the individual symptoms are identified, the user may be able to infer certain fixes that might help the drive to perform better. For example, if the Symptom Radar Chart shows that the Drive Detected Issues axis has a score below 10, and subsequent investigation shows that the Number of ASR Events has risen several times over the past week, then the user might infer that there could be an issue with the drive interface cable, and try swapping the cable. Similarly, if the Symptom Radar Chart shows that the Temperature axis has a score below 10, and subsequent investigation shows that the Time in Over Temperature has frequently increased in the past week, then the user might infer that there might be a temperature control issue and try putting the drive enclosure in a cooler room.
DA Drive Analyzer Now Available for ASUSTOR NAS Users
QNAP Launches the AI-Powered DA Drive Analyzer 2.0 – Predicts NAS Drive Failure Within 24 Hours & Enhances Enterprise Privacy
Photo Credit: Ankabala
Recent Comments