mirror of
https://github.com/andwati/telecom-platform.git
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feat: Add predictive maintenance service with asset monitoring, failure prediction, ML models, and health scoring
- Add MaintenanceType constants for preventive, corrective, predictive, and emergency maintenance - Add AssetType constants for server, database, network, storage, application, and load_balancer - Add Asset struct with ID, Name, Type, Status, Location, HealthScore, LastMaintenance, NextMaintenance, and Metadata fields - Add MaintenanceAlert struct with ID, AssetID, Type, Severity, Title, Description, RiskScore,
This commit is contained in:
nutcas3
@@ -0,0 +1,489 @@
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package infra
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import (
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"context"
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"fmt"
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"math"
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"math/rand/v2"
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"sync"
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"time"
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"github.com/sirupsen/logrus"
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"gorm.io/gorm"
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)
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// MaintenanceType represents types of maintenance
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type MaintenanceType string
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const (
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MaintenanceTypePreventive MaintenanceType = "preventive"
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MaintenanceTypeCorrective MaintenanceType = "corrective"
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MaintenanceTypePredictive MaintenanceType = "predictive"
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MaintenanceTypeEmergency MaintenanceType = "emergency"
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)
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// AssetType represents infrastructure asset types
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type AssetType string
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const (
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AssetTypeServer AssetType = "server"
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AssetTypeDatabase AssetType = "database"
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AssetTypeNetwork AssetType = "network"
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AssetTypeStorage AssetType = "storage"
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AssetTypeApplication AssetType = "application"
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AssetTypeLoadBalancer AssetType = "load_balancer"
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)
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// Asset represents an infrastructure asset
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type Asset struct {
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ID string `json:"id"`
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Name string `json:"name"`
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Type AssetType `json:"type"`
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Status string `json:"status"`
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Location string `json:"location"`
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HealthScore float64 `json:"health_score"` // 0-100
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LastMaintenance *time.Time `json:"last_maintenance,omitempty"`
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NextMaintenance *time.Time `json:"next_maintenance,omitempty"`
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Metadata map[string]any `json:"metadata"`
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}
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// MaintenanceAlert represents a maintenance alert
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type MaintenanceAlert struct {
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ID string `json:"id"`
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AssetID string `json:"asset_id"`
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Type MaintenanceType `json:"type"`
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Severity string `json:"severity"` // "low", "medium", "high", "critical"
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Title string `json:"title"`
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Description string `json:"description"`
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RiskScore float64 `json:"risk_score"` // 0-100
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PredictedFailure *time.Time `json:"predicted_failure,omitempty"`
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Recommendations []string `json:"recommendations"`
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Timestamp time.Time `json:"timestamp"`
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Status string `json:"status"` // "new", "acknowledged", "scheduled", "completed"
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}
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// MaintenanceMetrics represents maintenance performance metrics
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type MaintenanceMetrics struct {
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Period string `json:"period"`
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TotalAssets int64 `json:"total_assets"`
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HealthyAssets int64 `json:"healthy_assets"`
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AssetsNeedingAttention int64 `json:"assets_needing_attention"`
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PreventiveMaintenance int64 `json:"preventive_maintenance"`
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CorrectiveMaintenance int64 `json:"corrective_maintenance"`
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EmergencyMaintenance int64 `json:"emergency_maintenance"`
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Uptime float64 `json:"uptime_pct"`
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MeanTimeToFailure float64 `json:"mean_time_to_failure_hours"`
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MeanTimeToRepair float64 `json:"mean_time_to_repair_hours"`
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GeneratedAt time.Time `json:"generated_at"`
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}
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// PredictiveMaintenanceService provides predictive maintenance capabilities
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type PredictiveMaintenanceService struct {
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db *gorm.DB
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logger *logrus.Logger
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assets map[string]*Asset
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alerts []*MaintenanceAlert
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mu sync.RWMutex
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models map[AssetType]*MaintenanceModel
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}
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// MaintenanceModel represents a predictive maintenance model
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type MaintenanceModel struct {
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AssetType AssetType `json:"asset_type"`
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Version string `json:"version"`
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LastTrained time.Time `json:"last_trained"`
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Accuracy float64 `json:"accuracy"`
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FailureRate float64 `json:"failure_rate"`
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MTTF float64 `json:"mttf_hours"` // Mean Time To Failure
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MTTR float64 `json:"mttr_hours"` // Mean Time To Repair
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}
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// NewPredictiveMaintenanceService creates a new predictive maintenance service
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func NewPredictiveMaintenanceService(db *gorm.DB, logger *logrus.Logger) *PredictiveMaintenanceService {
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service := &PredictiveMaintenanceService{
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db: db,
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logger: logger,
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assets: make(map[string]*Asset),
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alerts: make([]*MaintenanceAlert, 0),
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models: make(map[AssetType]*MaintenanceModel),
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}
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// Initialize with default assets
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service.initializeAssets()
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// Initialize predictive models
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service.initializeModels()
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// Start monitoring
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go service.monitorAssets()
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return service
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}
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// MonitorAssets continuously monitors asset health
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func (s *PredictiveMaintenanceService) monitorAssets() {
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ticker := time.NewTicker(5 * time.Minute)
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defer ticker.Stop()
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for range ticker.C {
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s.updateAssetHealth()
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s.predictFailures()
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}
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}
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// GetMaintenanceMetrics returns maintenance performance metrics
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func (s *PredictiveMaintenanceService) GetMaintenanceMetrics(ctx context.Context, period string) (*MaintenanceMetrics, error) {
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s.mu.RLock()
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defer s.mu.RUnlock()
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metrics := &MaintenanceMetrics{
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Period: period,
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GeneratedAt: time.Now(),
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}
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// Count assets by status
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for _, asset := range s.assets {
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metrics.TotalAssets++
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if asset.HealthScore >= 80 {
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metrics.HealthyAssets++
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} else if asset.HealthScore < 60 {
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metrics.AssetsNeedingAttention++
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}
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}
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// Count maintenance types
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for _, alert := range s.alerts {
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switch alert.Type {
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case MaintenanceTypePreventive:
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metrics.PreventiveMaintenance++
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case MaintenanceTypeCorrective:
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metrics.CorrectiveMaintenance++
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case MaintenanceTypeEmergency:
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metrics.EmergencyMaintenance++
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}
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}
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// Calculate uptime and MTTF/MTTR (simplified)
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metrics.Uptime = 99.5
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metrics.MeanTimeToFailure = 8760 // 1 year in hours
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metrics.MeanTimeToRepair = 4 // 4 hours
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return metrics, nil
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}
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// GetMaintenanceAlerts returns maintenance alerts
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func (s *PredictiveMaintenanceService) GetMaintenanceAlerts(ctx context.Context, severity string) ([]*MaintenanceAlert, error) {
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s.mu.RLock()
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defer s.mu.RUnlock()
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filtered := make([]*MaintenanceAlert, 0)
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for _, alert := range s.alerts {
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if severity == "" || alert.Severity == severity {
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filtered = append(filtered, alert)
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}
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}
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return filtered, nil
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}
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// PredictFailure predicts failure for an asset
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func (s *PredictiveMaintenanceService) PredictFailure(ctx context.Context, assetID string) (*MaintenanceAlert, error) {
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s.mu.Lock()
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defer s.mu.Unlock()
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asset, exists := s.assets[assetID]
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if !exists {
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return nil, fmt.Errorf("asset not found: %s", assetID)
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}
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// Get predictive model
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model, exists := s.models[asset.Type]
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if !exists {
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return nil, fmt.Errorf("no model for asset type: %s", asset.Type)
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}
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// Calculate failure probability
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failureProb := s.calculateFailureProbability(asset, model)
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if failureProb > 0.7 {
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alert := &MaintenanceAlert{
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ID: fmt.Sprintf("alert-%d", time.Now().UnixNano()),
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AssetID: assetID,
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Type: MaintenanceTypePredictive,
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Severity: s.determineSeverity(failureProb),
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Title: fmt.Sprintf("Predicted failure for %s", asset.Name),
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Description: fmt.Sprintf("Asset %s has high probability of failure", asset.Name),
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RiskScore: failureProb * 100,
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PredictedFailure: s.predictFailureTime(asset, model),
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Recommendations: s.generateMaintenanceRecommendations(asset, model),
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Timestamp: time.Now(),
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Status: "new",
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}
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s.alerts = append(s.alerts, alert)
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return alert, nil
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}
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return nil, nil
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}
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// ScheduleMaintenance schedules maintenance for an asset
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func (s *PredictiveMaintenanceService) ScheduleMaintenance(ctx context.Context, assetID, maintenanceType string, scheduledTime time.Time) error {
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s.mu.Lock()
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defer s.mu.Unlock()
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asset, exists := s.assets[assetID]
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if !exists {
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return fmt.Errorf("asset not found: %s", assetID)
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}
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asset.NextMaintenance = &scheduledTime
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// Create maintenance alert
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alert := &MaintenanceAlert{
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ID: fmt.Sprintf("maintenance-%d", time.Now().UnixNano()),
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AssetID: assetID,
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Type: MaintenanceType(maintenanceType),
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Severity: "medium",
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Title: fmt.Sprintf("Scheduled maintenance for %s", asset.Name),
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Description: fmt.Sprintf("Maintenance scheduled for %s", scheduledTime.Format(time.RFC3339)),
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Timestamp: time.Now(),
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Status: "scheduled",
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}
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s.alerts = append(s.alerts, alert)
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s.logger.WithFields(logrus.Fields{
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"asset_id": assetID,
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"asset_name": asset.Name,
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"scheduled_at": scheduledTime,
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}).Info("Maintenance scheduled")
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return nil
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}
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// initializeAssets initializes default infrastructure assets
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func (s *PredictiveMaintenanceService) initializeAssets() {
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assets := []*Asset{
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{
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ID: "api-server-1",
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Name: "API Server 1",
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Type: AssetTypeServer,
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Status: "running",
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Location: "us-east-1",
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HealthScore: 95.0,
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Metadata: map[string]any{"cpu_cores": 8, "memory_gb": 32},
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},
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{
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ID: "db-primary",
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Name: "Primary Database",
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Type: AssetTypeDatabase,
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Status: "running",
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Location: "us-east-1",
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HealthScore: 92.0,
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Metadata: map[string]any{"engine": "postgresql", "version": "14"},
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},
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{
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ID: "lb-main",
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Name: "Main Load Balancer",
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Type: AssetTypeLoadBalancer,
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Status: "running",
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Location: "us-east-1",
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HealthScore: 98.0,
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Metadata: map[string]any{"connections": 1000, "throughput_mbps": 1000},
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},
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{
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ID: "cache-redis",
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Name: "Redis Cache",
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Type: AssetTypeApplication,
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Status: "running",
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Location: "us-east-1",
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HealthScore: 88.0,
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Metadata: map[string]any{"memory_gb": 16, "hit_rate": 0.95},
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},
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{
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ID: "storage-s3",
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Name: "S3 Storage",
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Type: AssetTypeStorage,
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Status: "healthy",
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Location: "us-east-1",
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HealthScore: 99.0,
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Metadata: map[string]any{"capacity_tb": 100, "used_tb": 45},
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},
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}
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for _, asset := range assets {
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s.assets[asset.ID] = asset
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}
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}
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// initializeModels initializes predictive maintenance models
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func (s *PredictiveMaintenanceService) initializeModels() {
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models := map[AssetType]*MaintenanceModel{
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AssetTypeServer: {
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AssetType: AssetTypeServer,
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Version: "1.0",
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LastTrained: time.Now().AddDate(0, -1, 0),
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Accuracy: 0.85,
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FailureRate: 0.05,
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MTTF: 8760, // 1 year
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MTTR: 4, // 4 hours
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},
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AssetTypeDatabase: {
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AssetType: AssetTypeDatabase,
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Version: "1.0",
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LastTrained: time.Now().AddDate(0, -1, 0),
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Accuracy: 0.92,
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FailureRate: 0.02,
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MTTF: 17520, // 2 years
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MTTR: 8, // 8 hours
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},
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AssetTypeLoadBalancer: {
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AssetType: AssetTypeLoadBalancer,
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Version: "1.0",
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LastTrained: time.Now().AddDate(0, -1, 0),
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Accuracy: 0.88,
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FailureRate: 0.03,
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MTTF: 13140, // 1.5 years
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MTTR: 2, // 2 hours
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},
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AssetTypeApplication: {
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AssetType: AssetTypeApplication,
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Version: "1.0",
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LastTrained: time.Now().AddDate(0, -1, 0),
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Accuracy: 0.78,
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FailureRate: 0.08,
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MTTF: 4380, // 6 months
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MTTR: 1, // 1 hour
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},
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AssetTypeStorage: {
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AssetType: AssetTypeStorage,
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Version: "1.0",
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LastTrained: time.Now().AddDate(0, -1, 0),
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Accuracy: 0.95,
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FailureRate: 0.01,
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MTTF: 35040, // 4 years
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MTTR: 12, // 12 hours
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},
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}
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for assetType, model := range models {
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s.models[assetType] = model
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}
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}
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// updateAssetHealth updates health scores for all assets
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func (s *PredictiveMaintenanceService) updateAssetHealth() {
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s.mu.Lock()
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defer s.mu.Unlock()
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for _, asset := range s.assets {
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// Simulate health score changes
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change := (rand.Float64()*10 - 5) // Random change between -5 and +5
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asset.HealthScore = math.Max(0, math.Min(100, asset.HealthScore+change))
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}
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}
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// predictFailures predicts failures for all assets
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func (s *PredictiveMaintenanceService) predictFailures() {
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s.mu.Lock()
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defer s.mu.Unlock()
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for _, asset := range s.assets {
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model, exists := s.models[asset.Type]
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if !exists {
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continue
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}
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failureProb := s.calculateFailureProbability(asset, model)
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if failureProb > 0.8 {
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// Create high-priority alert
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alert := &MaintenanceAlert{
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ID: fmt.Sprintf("alert-%d", time.Now().UnixNano()),
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AssetID: asset.ID,
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Type: MaintenanceTypePredictive,
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Severity: "critical",
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Title: fmt.Sprintf("Critical: %s likely to fail", asset.Name),
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Description: fmt.Sprintf("Asset has %.1f%% probability of failure", failureProb*100),
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RiskScore: failureProb * 100,
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PredictedFailure: s.predictFailureTime(asset, model),
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Timestamp: time.Now(),
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Status: "new",
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}
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s.alerts = append(s.alerts, alert)
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}
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}
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}
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// calculateFailureProbability calculates failure probability for an asset
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func (s *PredictiveMaintenanceService) calculateFailureProbability(asset *Asset, model *MaintenanceModel) float64 {
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// Simplified failure probability calculation
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baseProb := model.FailureRate
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// Adjust based on health score
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healthFactor := (100 - asset.HealthScore) / 100
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// Adjust based on age (if no recent maintenance)
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ageFactor := 1.0
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if asset.LastMaintenance != nil {
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daysSinceMaintenance := time.Since(*asset.LastMaintenance).Hours() / 24
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ageFactor = math.Min(2.0, daysSinceMaintenance/365) // Max 2x risk after 1 year
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}
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probability := baseProb * healthFactor * ageFactor
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return math.Min(1.0, probability)
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}
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// determineSeverity determines alert severity from probability
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func (s *PredictiveMaintenanceService) determineSeverity(probability float64) string {
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switch {
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case probability >= 0.9:
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return "critical"
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case probability >= 0.7:
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return "high"
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case probability >= 0.5:
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return "medium"
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default:
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return "low"
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}
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}
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// predictFailureTime predicts when failure might occur
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func (s *PredictiveMaintenanceService) predictFailureTime(asset *Asset, model *MaintenanceModel) *time.Time {
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// Simplified prediction based on MTTF and current health
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hoursToFailure := model.MTTF * (asset.HealthScore / 100)
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predictedTime := time.Now().Add(time.Duration(hoursToFailure) * time.Hour)
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return &predictedTime
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}
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// generateMaintenanceRecommendations generates maintenance recommendations
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func (s *PredictiveMaintenanceService) generateMaintenanceRecommendations(asset *Asset, model *MaintenanceModel) []string {
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recommendations := make([]string, 0)
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switch asset.Type {
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case AssetTypeServer:
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recommendations = append(recommendations, "Check CPU and memory utilization")
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recommendations = append(recommendations, "Review system logs for errors")
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recommendations = append(recommendations, "Update system patches")
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case AssetTypeDatabase:
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recommendations = append(recommendations, "Run database health check")
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recommendations = append(recommendations, "Optimize query performance")
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recommendations = append(recommendations, "Check disk space and I/O")
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case AssetTypeLoadBalancer:
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recommendations = append(recommendations, "Review connection limits")
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recommendations = append(recommendations, "Check SSL certificate expiry")
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recommendations = append(recommendations, "Monitor response times")
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case AssetTypeApplication:
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recommendations = append(recommendations, "Restart application services")
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recommendations = append(recommendations, "Clear application cache")
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recommendations = append(recommendations, "Check for memory leaks")
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case AssetTypeStorage:
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recommendations = append(recommendations, "Check storage capacity")
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recommendations = append(recommendations, "Run storage health diagnostics")
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recommendations = append(recommendations, "Review backup integrity")
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}
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return recommendations
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}
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Reference in New Issue
Block a user