Having managed both traditional rack servers and blade systems across multiple data centers, I've observed that chassis failures do occur - though modern systems have significantly improved. HP's current generation (like the Synergy platform) boasts 99.999% chassis uptime in their spec sheets, but real-world performance differs.

The main failure points in blade chassis aren't the blades themselves but:

• Midplane/backplane connectivity
• Chassis management module (CMM)
• Power distribution units
• Cooling systems

Here's sample code I use to monitor chassis health through HP's REST API:

import requests
from hpOneView.oneview_client import OneViewClient

config = {
    "ip": "chassis_mgmt_ip",
    "credentials": {
        "userName": "admin",
        "password": "password"
    }
}

oneview_client = OneViewClient(config)
chassis_health = oneview_client.chassis.get_environmental_metrics()
if chassis_health['fanRedundancy'] != 'Redundant' or 
   chassis_health['powerSupplyRedundancy'] != 'Redundant':
    trigger_alert()

For your Ethiopia deployment, consider these mitigation strategies:

1. Maintain spare power supplies on-site (they're often hot-swappable)
2. Implement cross-chassis virtualization (like VMware's vSAN stretched cluster)
3. Use chassis with modular components (HP's "Composable Infrastructure" approach)

Instead of full chassis redundancy, we've successfully implemented:

• N+1 power supplies across multiple chassis
• Shared spare blades between chassis
• Cloud failover for critical workloads

For development environments or non-critical workloads, we've run single chassis deployments with:

• Automated chassis configuration backups
• Pre-staged spare chassis at regional hubs
• 24/7 vendor support contracts with 4-hour response

Blade server architectures introduce single points of failure that simply don't exist in traditional rack deployments. The chassis becomes mission-critical infrastructure housing:

• Shared power distribution (midplane/backplane)
• Common cooling infrastructure
• Unified management controllers
• Network switching modules

Vendor MTBF claims often exceed 100,000 hours, but real-world data tells a different story. A 2022 Uptime Institute survey of 300 datacenters showed:

// Sample data structure representing failure statistics
const bladeFailureData = {
  chassisRelatedOutages: 23%,
  powerSupplyFailures: 41%,
  midplaneIssues: 18%,
  coolingSystemFailures: 12%,
  other: 6%
};

These components deserve special attention in redundancy planning:

# Critical chassis components checklist
CRITICAL_COMPONENTS = [
    "Power supply units (PSUs)",
    "Chassis management module", 
    "Fan assemblies",
    "Midplane connectors",
    "Fabric modules"
]

For high-availability deployments in challenging locations like Ethiopia:

// Recommended redundancy configuration
const redundancyConfig = {
  minChassisCount: 2,
  powerSuppliesPerChassis: {
    installed: 4,
    required: 2,
    nPlus: 2
  },
  cooling: {
    fans: "N+2 redundancy",
    zones: "Dual cooling zones"
  },
  management: "Active/standby controllers"
};

Shared storage introduces additional failure domains. Consider:

# Storage architecture decision tree
if reliability_required > 99.99%:
    implement_multi_path_io()
    use_synchronous_replication()
    require_dual_controllers()
elif location_constraints:
    implement_hyperconverged_storage()
    use_erasure_coding()

HP's BladeSystem c7000 shows these common failure patterns:

• OA (Onboard Administrator) module firmware issues
• Interconnect bay backplane failures
• Power supply firmware compatibility problems

For locations with limited vendor support:

// Automated spare parts alert system
class SparePartsMonitor {
  constructor(components) {
    this.thresholds = {
      psu: 2,
      fan: 3,
      controller: 1  
    };
  }
  
  checkInventory(location) {
    // Integration with local suppliers API
    return this.getLocalAvailability(location);
  }
}